Graph data is increasingly prevalent across domains, offering analytical value but raising significant privacy concerns. Edges may encode sensitive relationships, while node attributes may contain sensitive entity or personal data. Differential Privacy (DP) has gained traction for its strong guarantees, yet applying DP to graphs is challenging because of their complex relational structure, leading to trade-offs between privacy and utility. Existing methods vary in privacy definitions, utility goals, and contextual settings, complicating comparison. For practitioners, this is compounded by DP's interpretability issues, contributing to misleading protection claims.

Poisoning attacks have been shown to pose significant threats to federated learning (FL), including both untargeted and targeted attacks. To mitigate such threats, the majority of existing defenses are implemented on the server side during the aggregation process. However, as data remains inherently local in FL, these defenses either rely on unreliable statistical or structural properties of local updates, or make strong assumptions that rely on dataset information. As a result, the unique advantage of clients having access to trusted local data and training dynamics has been largely overlooked. In this work, we propose ClieND1, a novel client-side detection framework that shifts the detection of poisoning attacks from the server to the client. Specifically, ClieND enables each client to maintain the neuron importance scores of the aggregated global model in each round by leveraging its local dataset. Through tracking the inter-round changes in these scores, each client detects abnormal behavior by identifying significant discrepancy spikes, which serve as indicators of potential poisoning attacks. To evaluate the effectiveness of ClieND, we compare it against three state-of-the-art baselines under both targeted and untargeted poisoning attacks, and also assess its ability to detect attacks at an early stage. Experimental results show that ClieND achieves a false positive rate (FPR) as low as 0.01 and a true positive rate (TPR) of up to 0.99, which significantly outperforms server-side defenses, demonstrating its high detection accuracy. Additionally, the existence of poisoning attacks, even those launched by adaptive settings, can be detected in an early stage within 5 communication rounds.

The critical energy infrastructure is undergoing two significant transformations: the rapid increase in renewable distributed energy resources (DER) and the digitalization of the energy sector, collectively shaping what is known as the Internet of Energy (IoE). Artificial intelligence (AI) has become a widely adopted tool for effectively allocating energy and managing sector-related resources, where ensuring fairness is essential. While inherent unfairness in AI systems is well acknowledged, little attention has been given to evaluating this unfairness and its real-world implications within the context of the IoE. In this study, we take a first step to elucidate the unfairness in AI-powered IoE systems induced by malicious users. We introduce Unfairness Score (UScore), a novel metric designed to evaluate the unfairness of machine learning models in real-world IoE scenarios. We then extensively evaluate unfairness attacks using three IoE tabular datasets, demonstrating that AI model fairness can be compromised through data poisoning, whether in centralized learning (CL) or federated learning (FL) settings. Notably, such compromises can occur when malicious users tamper with only a small subset of the data they control. Finally, we propose a novel approach that unifies fairness and differential privacy (DP) by leveraging DP as a provable defense mechanism. This approach provides a universally applicable solution to unfairness attacks, regardless of whether the learning tasks are classification or regression, and is effective in both FL and CL settings. Our contributions represent a significant step in addressing unfairness and privacy concerns in AI-powered IoE systems.

Software vulnerabilities (SVs) have emerged as a prevalent and crucial concern for safety-critical systems. This has spurred significant advancements in utilizing AI-based methods, including machine learning and deep learning, for software vulnerability detection (SVD). While AI-based methods have shown promising performance in SVD, their effectiveness on real-world, complex, and diverse source code datasets remains limited in practice. To tackle this challenge, in this paper, we propose a novel framework that enhances the capability of large language models to learn and utilize semantic and syntactic relationships from source code data for SVD. As a result, our proposed SAFE approach can enable the acquisition of fundamental knowledge from source code data while adeptly utilizing crucial relationships, i.e., semantic and syntactic associations, to improve the effectiveness of solving the SVD problem. The rigorous and extensive experimental results on three real-world challenging datasets (i.e., Devign, ReVeal, and D2A) demonstrate the superiority of our approach over eight effective and state-of-the-art baselines. In summary, on average, our SAFE approach achieves higher performances from 4.79% to 11.57% for F1-measure and from 16.93% to 26.24% for Recall compared to the baseline methods across all the datasets used.

The exploration of backdoor vulnerabilities in object detectors, particularly in real-world scenarios, remains limited. A significant challenge lies in the absence of a natural physical backdoor dataset, and constructing such a dataset is both time- and labor-intensive. In this work, we address this gap by creating a large-scale dataset comprising approximately 11,800 images/frames with annotations featuring natural objects (e.g., T-shirts and hats) as triggers to incur cloaking adversarial effects in diverse real-world scenarios. This dataset is tailored for the study of physical backdoors in object detectors. Leveraging this dataset, we conduct a comprehensive evaluation of an insidious cloaking backdoor effect against object detectors, wherein the bounding box around a person vanishes when the individual is near a natural object (e.g., a commonly available T-shirt) in front of the detector. Our evaluations encompass three prevalent attack surfaces: data outsourcing, model outsourcing, and the use of pretrained models. The cloaking effect is successfully implanted in object detectors across all three attack surfaces. We extensively evaluate four popular object detection algorithms (anchor-based Yolo-V3, Yolo-V4, Faster R-CNN, and anchor-free CenterNet) using 19 videos (totaling approximately 11,800 frames) in real-world scenarios. Our results demonstrate that the backdoor attack exhibits remarkable robustness against various factors, including movement, distance, angle, non-rigid deformation, and lighting. In data and model outsourcing scenarios, the attack success rate (ASR) in most videos reaches 100% or near it, while the clean data accuracy of the backdoored model remains indistinguishable from that of the clean model, making it impossible to detect backdoor behavior through a validation set. Notably, two-stage object detectors (e.g., Faster R-CNN) show greater resistance to backdoor attacks under pure data poisoning conditions (i.e., in data outsourcing) compared to one-stage detectors (e.g., the Yolo series). However, this challenge is surmountable when the attacker controls the training process (particularly in model outsourcing), even with the same small poisoning rate budget as in data outsourcing. In transfer learning attack scenarios assessed on CenterNet, the average ASR remains high at 78%. A detailed 5-minute video illustrating our attack is available at https://youtu.be/Q3HOF4OobbY.

Content providers increasingly utilise Content Delivery Networks (CDNs) to enhance users' content download experience. However, this deployment scenario raises significant security concerns regarding content confidentiality and user privacy due to the involvement of third-party providers. Prior proposals using private information retrieval (PIR) and oblivious RAM (ORAM) have proven impractical due to high computation and communication costs, as well as integration challenges within distributed CDN architectures. In response, we present \textsf{OblivCDN}, a practical privacy-preserving system meticulously designed for seamless integration with the existing real-world Internet-CDN infrastructure. Our design strategically adapts Range ORAM primitives to optimise memory and disk seeks when accessing contiguous blocks of CDN content, both at the origin and edge servers, while preserving both content confidentiality and user access pattern hiding features. Also, we carefully customise several oblivious building blocks that integrate the distributed trust model into the ORAM client, thereby eliminating the computational bottleneck in the origin server and reducing communication costs between the origin server and edge servers. Moreover, the newly-designed ORAM client also eliminates the need for trusted hardware on edge servers, and thus significantly ameliorates the compatibility towards networks with massive legacy devices.In real-world streaming evaluations, OblivCDN} demonstrates remarkable performance, downloading a $256$ MB video in just $5.6$ seconds. This achievement represents a speedup of $90\times$ compared to a strawman approach (direct ORAM adoption) and a $366\times$ improvement over the prior art, OblivP2P.

In an era where misinformation spreads freely, factchecking (FC) plays a crucial role in verifying claims and promoting reliable information.While automated fact-checking (AFC) has advanced significantly, existing systems remain vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs.These attacks can distort the truth, mislead decision-makers, and ultimately undermine the reliability of FC models.Despite growing research interest in adversarial attacks against AFC systems, a comprehensive, holistic overview of key challenges remains lacking.These challenges include understanding attack strategies, assessing the resilience of current models, and identifying ways to enhance robustness.This survey provides the first in-depth review of adversarial attacks targeting FC 1 , categorizing existing attack methodologies and evaluating their impact on AFC systems.Additionally, we examine recent advancements in adversary-aware defenses and highlight open research questions that require further exploration.Our findings underscore the urgent need for resilient FC frameworks capable of withstanding adversarial manipulations in pursuit of preserving high verification accuracy.Attack Target Edit Granularity Attack Technique Claim attack Evidence attack Claim-evidence pair attack Generate Manipulate Generate Manipulate

Phishing attacks remain a significant challenge for detection, explanation, and defense, despite over a decade of research on both technical and non-technical solutions. AI-based phishing detection methods are among the most effective approaches for defeating phishing attacks, providing predictions on the vulnerability label (i.e., phishing or benign) of data. However, they often lack intrinsic explainability, failing to identify the specific information that triggers the classification. To this end, we propose AI2TALE, an innovative deep learning-based approach for email (the most common phishing medium) phishing attack localization. Our method aims to not only predict the vulnerability label of the email data but also provide the capability to automatically learn and identify the most important and phishing-relevant information (i.e., sentences) in the phishing email data, offering useful and concise explanations for the identified vulnerability. Extensive experiments on seven diverse real-world email datasets demonstrate the capability and effectiveness of our method in selecting crucial information, enabling accurate detection and offering useful and concise explanations (via the most important and phishing-relevant information triggering the classification) for the vulnerability of phishing emails. Notably, our approach outperforms state-of-the-art baselines by 1.5% to 3.5% on average in Label-Accuracy and Cognitive-True-Positive metrics under a weakly supervised setting, where only vulnerability labels are used without requiring ground truth phishing information.

The presence of non-independent and identically distributed (non-IID) data among clients poses a critical challenge to the deployment of Federated Learning (FL) in practice. In response, state-of-the-art solutions known as Clustered Federated Learning (CFL) schemes, such as FL+HC and PACFL, have emerged to tackle this issue. Their main innovation is to cluster non-IID clients into groups of IID clients, such that techniques designated for IID scenarios can be easily applicable. Nonetheless, the robustness of CFL schemes remains largely unexplored, and existing Byzantine-robust defence mechanisms prove inadequate in CFL schemes and non-IID data settings. In this work, we present novel powerful CFL-specific poisoning attacks, named Cluster-U-M and Cluster-U-D. These attacks are designed to significantly reduce the model utility, measured in terms of test accuracy, for benign clients participating in the CFL schemes. Notably, these attacks remain agnostic, requiring no adversarial knowledge regarding defense solutions and benign clients themselves. At a high level, the attacks involve two steps, including cluster poisoning attacks and client-drift exploitation within clusters. The former induces the grouping of clients with different training distributions, and the latter amplifies the difference between each client's optimum and their group's average aggregation. We extensively evaluate the impact of these attacks using FL+HC and PACFL schemes on both small and large scales. The evaluation results demonstrate that the attacks can compromise up to 54% of clients, with a maximum accuracy loss of 48%. Even with only 0.1% clients compromised, which represents a minimal practical adversarial effort, these attacks can still victimize around 4% clients. We evaluate the effectiveness of two state-of-the-art Byzantine-robust defence mechanisms, i.e., FLTrust and FLAME, in countering Cluster-U-M and Cluster-U-D, and find that the attacks can victimize up to 38% of clients with an accuracy loss of 18-38% under the FL+HC scheme.

The past decade has witnessed the wide adoption of AI code generators, such as GitHub Copilot, AskCodi, and OpenAI Codex. They offer intelligent solution code for code completion to achieve faster development, cleaner code, and a significant boost in overall productivity. However, such significant productivity advantages also inadvertently lead to the generation of insecure solution code because most AI code generators derive their knowledge from existing projects, which typically prioritize functionality over security. Although numerous tools have been developed to integrate with the code generators for identifying vulnerabilities, the inconsistency in syntactic features and variability in coding rules make the detection task challenging across different programming languages. To address the challenges, we devise a prompt-enhancing approach, PECKER. It examines textual prompts provided by users to identify risky prompts that could lead to insecure code generation. Given the risky prompts, PECKER conducts security-centric rewriting to strengthen the "potentially insecure" descriptions, thereby guiding AI code generators in mitigating vulnerabilities during code generation. We integrated PECKER with one of the most prevalent AI code generators, GitHub Copilot for evaluation. Among 509 risky prompts, PECKER successfully identified and rectified 471 risky prompts.

The increasing complexity of decision-making in dynamic environments, particularly in high-stakes domains like cybersecurity, demands more than automated solutions—it requires effective integration of human expertise with advanced AI capabilities. While approaches like ensemble learning and Mixture of Experts (MoE) enhance automated decision-making, they struggle with handling uncertainty and novel scenarios. Techniques such as learning to defer and learning to complement mitigate this by incorporating human input, but assume that a definitive expert is always available—an assumption that often fails in real-world settings. To bridge this gap, we introduce A 2 C , a modular, multi-stage collaborative decision-making framework that enhances adaptability and decision robustness under uncertainty by seamlessly transitioning between three decision-making modes: Automated, Augmented Deferral, and Collaborative Exploration (CoEx). A key innovation of CoEX is its ability to handle cases where both AI and human experts face uncertainty, overcoming a critical limitation of traditional deferral systems. We validate A 2 C through experiments on benchmark datasets, Large Language Model (LLM) simulations of human–AI collaboration, and real-world human–AI interaction studies with cybersecurity researchers. Results show that A 2 C consistently outperforms conventional approaches that rely solely on full automation or selective human intervention, demonstrating its potential as a practical and scalable solution for expert decision-making in complex domains. For image detection on CIFAR-10, detection rates improved from 37.8% with automation alone to 64.75% with augmented deferral, and further to 92.95% with collaborative exploration. Similarly, for intrusion detection on KDDCup, rates rose from 33.43% with automation to 35.18% with augmented deferral, and finally reached 87.04% with CoEx, highlighting its effectiveness in handling uncertainty.

Next-generation networks demand security that evolves as fast as threats do. Our pioneering decentralized Zero Trust Architecture (dZTA), proposed in this paper, redefines protection for IoT and remote collaboration, merging Zero Trust’s ironclad access controls with blockchain’s transparency and federated learning’s privacy-first analytics. Unlike traditional models, dZTA enforces security at every layer: a distributed policy engine eliminates single points of failure, cross-network analytics optimize WiFi-8, satellite, and 6G performance under real-world stressors, and anti-leakage protocols safeguard IoT ecosystems. Rigorous real-world simulations confirm dZTA’s dual triumph—uncompromising security and seamless efficiency— proving its readiness to secure tomorrow’s hyperconnected world.

With frequent software-induced activations on DRAM rows, bit flips can occur on their physically adjacent rows (i.e., RowHammer). Existing studies leverage FPGA platforms to characterize RowHammer, which have identified key factors that contribute to RowHammer bit flips, e.g., data pattern. As the FPGA-based studies have removed the interference of the OS and the memory controller, their findings on the identified contributing factors do not always work as reported in a real-world computing system, resulting in negative effects on system-level RowHammer attacks and defenses. In this paper, we carry out a system-level empirical study on factors from both the software side and the DRAM side that contribute to RowHammer. We conduct the study on 33 DRAM modules including both DDR4 and DDR3, with 292 DRAM chips from various vendors. Our experimental results from the software side show that some prior findings about existing factors are inconsistent with our observations, thus not applicable to a real-world system. Also, we contribute to identifying one new factor that effectively affects RowHammer bit flips. Our DRAM-side results identify three types of new contributing factors and indicate that DRAM modules are more vulnerable if they achieve better performance and lower power consumption. Particularly, Intel XMP, intended for improving DRAM performance, might be abused for RowHammer attacks.

AI-enabled critical infrastructures (ACIs) integrate artificial intelligence (AI) technologies into various essential systems and services that are vital to the functioning of society, offering significant implications for efficiency, security and resilience. While adopting decentralized AI approaches (such as federated learning technology) in ACIs is plausible, private and sensitive data are still susceptible to data reconstruction attacks through gradient optimization. In this work, we propose Compressed Differentially Private Aggregation (CDPA), a leakage-resilient, communication-efficient, and carbon-neutral approach for ACI networks. Specifically, CDPA has introduced a novel random bit-flipping mechanism as its primary innovation. This mechanism first converts gradients into a specific binary representation and then selectively flips masked bits with a certain probability. The proposed bit-flipping introduces a larger variance to the noise while providing differentially private protection and commendable efforts in energy savings while applying vector quantization techniques within the context of federated learning. The experimental evaluation indicates that CDPA can reduce communication cost by half while preserving model utility. Moreover, we demonstrate that CDPA can effectively defend against state-of-the-art data reconstruction attacks in both computer vision and natural language processing tasks. We highlight existing benchmarks that generate 2.6x to over 100x more carbon emissions than CDPA. We hope that the CDPA developed in this paper can inform the federated AI-enabled critical infrastructure of a more balanced trade-off between utility and privacy, resilience protection, as well as a better carbon offset with less communication overhead.

Critical infrastructures are increasingly integrating artificial intelligence (AI) technologies, including large language models (LLMs), into essential systems and services that are vital to societal functioning. Fine-tuning LLMs for specific domain tasks are crucial for their effective deployment in these contexts, but this process must carefully address both privacy and security concerns. Without proper safeguards, such integration can introduce additional risks, such as data leakage during training and diminished model trustworthiness due to the need for model compression to operate within limited bandwidth and computational capacity constraints. In this paper, we proposeHardening LLM Fine-tuning framework(HARDLLM), which addresses these challenges through two key components: (i) we develop a differentially private data selection method that ensures privacy protection by training the model exclusively on sampled and synthesized public data, thereby preventing any direct use of private data and enhancing leakage resilience throughout the training process, and (ii) we introduce a trustworthiness-aware model quantization approach to improve LLMs performance, such as reducing toxicity, enhancing adversarial robustness, and mitigating stereotypes, while maintaining negligible impact on model utility. Experimental results show that, the proposed algorithm ensures differential privacy when privacy budget is set at ϵ = 0.5, with only a 1% drop in accuracy, while other state-of-the-art methods experience an accuracy drop of at least 20% under the same privacy budget. Additionally, our quantization approach improves the trustworthiness of fine-tuned LLMs by an average of 3-4%, with only a negligible utility loss (approximately 1%) at a 50% compression rate.

Deceptive files, often called honeyfiles, have become an established tool in cyber security. Advances in machine learning (ML) models for content generation now allow the synthesis of deceptive material automatically and at scale. Metrics to quantify honeyfile attributes are thus essential to creating and evaluating effective deceptions. The two critical aspects of honeyfiles for which metrics are useful are enticement and realism . Enticement is the ability to attract the attention of intruders or users with malicious intent. Realism measures the similarity of deceptive artefacts to the objects they mimic. In the honeyfile literature, metrics for these attributes have been proposed: the Common Token Count (CTC) [ 1 ], and Topic Semantic Matching (TSM) [ 2 ] scores for enticement, and coherence and cohesion [ 3 ] for realism. In this study, we compare these metrics to the perceptions of human users exposed to text samples in a simulated data breach scenario on a crowd-sourcing platform. We recruited participants to judge the realism and enticement of honeyfile text generated using several techniques. The main findings are: (i) for the enticement metrics, TSM is aligned with the perceived enticement (p-value<0.001), while for the CTC score, we find inconsistent and inconclusive results, and (ii) for the realism metrics, cohesion and coherence, do not consistently align with perceived realism.

In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, which suffer from poor stability at high dimensions, or (2) multiple choice learning (MCL), an approach that uses M single-output functions, each only producing a point estimate hypothesis. This paper presents a Mixture of Multiple-Output functions (MoM) approach using a novel variant of dropout, Multiple Hypothesis Dropout. Unlike traditional MCL-based approaches, each multiple-output function not only estimates the mean but also the variance for its hypothesis. This is achieved through a novel stochastic winner-take-all loss which allows each multiple-output function to estimate variance through the spread of its subnetwork predictions. Experiments on supervised learning problems illustrate that our approach outperforms existing solutions for reconstructing multimodal output distributions. Additional studies on unsupervised learning problems show that estimating the parameters of latent posterior distributions within a discrete autoencoder significantly improves codebook efficiency, sample quality, precision and recall.

Recent works have shown that Federated Learning (FL) is vulnerable to backdoor attacks. Existing defenses cluster submitted updates from clients and select the best cluster for aggregation. However, they often rely on unrealistic assumptions regarding client submissions and sampled clients population while choosing the best cluster. We show that in realistic FL settings, state-of-the-art (SOTA) defenses struggle to perform well against backdoor attacks in FL. To address this, we highlight that backdoored submissions are adversarially biased and overconfident compared to clean submissions. We, therefore, propose an Adversarially Guided Stateful Defense (AGSD) against backdoor attacks on Deep Neural Networks (DNNs) in FL scenarios. AGSD employs adversarial perturbations to a small held-out dataset to compute a novel metric, called the trust index, that guides the cluster selection without relying on any unrealistic assumptions regarding client submissions. Moreover, AGSD maintains a trust state history of each client that adaptively penalizes backdoored clients and rewards clean clients. In realistic FL settings, where SOTA defenses mostly fail to resist attacks, AGSD mostly outperforms all SOTA defenses with minimal drop in clean accuracy (5% in the worst-case compared to best accuracy) even when (a) given a very small held-out dataset—typically AGSD assumes 50 samples (≤ 0.1% of the training data) and (b) no held-out dataset is available, and out-of-distribution data is used instead. For reproducibility, our code will be openly available at: https://github.com/hassanalikhatim/AGSD.

Rowhammer has drawn much attention from both academia and industry in the past years as rowhammer exploitation poses severe consequences to system security. Since the first comprehensive study of rowhammer in 2014, a number of rowhammer attacks have been demonstrated against dynamic random access memory (DRAM)-based commodity systems to break software confidentiality, integrity and availability. Accordingly, numerous software defenses have been proposed to mitigate rowhammer attacks on commodity systems of either legacy (e.g., DDR3) or recent DRAM (e.g., DDR4). Besides, multiple hardware defenses (e.g., Target Row Refresh) from the industry have been deployed into recent DRAM to eliminate rowhammer, which we categorize as production defenses.

Federated Learning (FL) is a privacy-preserving, collaborative machine learning technique where multiple clients train a shared model on their private datasets without sharing the data. While offering advantages, FL is susceptible to backdoor attacks, where attackers insert malicious model updates into the model aggregation process. Compromised models predict attacker-chosen targets when presented with specific attacker-defined inputs. Backdoor defences generally rely on anomaly detection techniques based on Differential Privacy (DP) or require legitimate clean test examples at the server. Anomaly detection-based defences can be defeated by stealth techniques and generally require inspection of client-submitted model updates. DP-based approaches tend to degrade the performance of the trained model due to excessive noise addition during training. Methods that require legitimate clean data on the server require strong assumptions about the task and may not be applicable in real-world settings. In this work, we view the question of backdoor attack robustness through the lens of loss function optimal points to build a defence that overcomes these limitations. We propose Mode Connectivity Based Federated Learning (MCFL), which leverages the recently discovered property of neural network loss surfaces, mode connectivity. We simulate backdoor attack scenarios using computer vision benchmark datasets, including CIFAR10, Fashion MNIST, MNIST, and Federated EMNIST. Our findings show that MCFL converges to high-quality models and effectively mitigates backdoor attacks relative to baseline defences from the literature without requiring inspection of client model updates or assuming clean data at the server.

All current backdoor attacks on deep learning (DL) models fall under the category of a vertical class backdoor (VCB).In VCB attacks, any sample from a class activates the implanted backdoor when the secret trigger is present, regardless of whether it is a sub-type source-class-agnostic backdoor or a source-class-specific backdoor. For example, a trigger of sunglasses could mislead a facial recognition model when either an arbitrary (source-class-agnostic) or a specific (source-class-specific) person wears sunglasses. Existing defense strategiesoverwhelmingly focus on countering VCB attacks, especially those that are source-class-agnostic. This narrow focus neglects the potential threat of other simpler yet general backdoor types, leading to false security implications. It is, therefore, crucial to discover and elucidate unknown backdoor types, particularly those that can be easily implemented, as a mandatory step before developing countermeasures.

Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) in the realm of computer vision, showcasing tremendous potential. However, recent research has un-veiled a susceptibility of ViTs to adversarial attacks, akin to their CNN counterparts. Adversarial training and randomization are two representative effective defenses for CNNs. Some researchers have attempted to apply adversarial training to ViTs and achieved comparable robustness to CNNs, while it is not easy to directly apply randomization to ViTs because of the architecture difference between CNNs and ViTs. In this paper, we delve into the structural intricacies of ViTs and propose a novel defense mechanism termed Random entangled image Transformer (ReiT), which seamlessly integrates adversarial training and randomization to bolster the adversarial robustness of ViTs. Recognizing the challenge posed by the structural disparities between ViTs and CNNs, we introduce a novel module, input-independent random entangled self-attention (II-ReSA). This module op-timizes random entangled tokens that lead to “dissimilar” self-attention outputs by leveraging model parameters and the sampled random tokens, thereby synthesizing the self-attention module outputs and random entangled tokens to diminish adversarial similarity. ReiT incorporates two distinct random entangled tokens and employs dual randomization, offering an effective countermeasure against adversarial examples while ensuring comprehensive deduction guarantees. Through extensive experiments conducted on various ViT variants and benchmarks, we substantiate the superiority of our proposed method in enhancing the adversarial robustness of Vision Transformers.

As we approach the physical limits of storage density, digital storage prices are no longer plummeting, despite the lingering belief that they still are. Meanwhile, data production continues to grow, making it harder to securely manage the data we produce. Typical digital storage media is often consumed by a small number of large files that are widely available on the web. If the availability of files on the web could be predicted, the choice between consuming local storage resources or simply redownloading the file in the future could be automated, thus increasing the efficiency of backup and encryption workflows. Through a large-scale analysis of hundreds of billions of crawl URLs spanning 8 years, as well as over 60 million HTTP header request responses from web servers, we explore the requirements and design of a framework for such predictions. It includes a data structure for efficiently representing the lateral/longitudinal availability of files and an extensible mathematical model for fast and adaptable prediction calculations. Additionally, we contribute novel observations about file availability on the web, including the identification of a period of initial volatility in their lifespans. Analysis indicates that a pool of 2,500TB of distributed, popular files is freely and predictably available to users, offering opportunities to reduce the storage and computational costs of both backup and encryption.

Deep Neural Network (DNN) models are often deployed in resource-sharing clouds as Machine Learning as a Service (MLaaS) to provide inference services. To steal model architectures that are of valuable intellectual properties, a class of attacks has been proposed via different side-channel leakage, posing a serious security challenge to MLaaS.Also targeting MLaaS, we propose a new end-to-end attack, DeepTheft, to accurately recover complex DNN model architectures on general processors via the RAPL (Running Average Power Limit)-based power side channel. While unprivileged access to the RAPL has been disabled in bare-metal OSes, we observe that the RAPL is still legitimately accessible in a platform as a service, e.g., the latest docker environment of version 20.10.18 used in this work. However, an attacker can acquire only a low sampling rate (1 KHz) of the time-series energy traces from the RAPL interface, rendering existing techniques ineffective in stealing large and deep DNN models. To this end, we design a novel and generic learning-based framework consisting of a set of meta-models, based on which DeepTheft is demonstrated to have high accuracy in recovering a large number (thousands) of models architectures from different model families including the deepest ResNet152. Particularly, DeepTheft has achieved a Levenshtein Distance Accuracy of 99.75% in recovering network structures, and a weighted average F1 score of 99.60% in recovering diverse layer-wise hyperparameters. Besides, our proposed learning framework is general to other time-series side-channel signals. To validate its generalization, another existing side channel is exploited, i.e., CPU frequency. Different from RAPL, CPU frequency is accessible to unprivileged users in bare-metal OSes. By using our generic learning framework trained against CPU frequency traces, DeepTheft has shown similarly high attack performance in stealing model architectures.

Web users often follow hyperlinks hastily, expecting them to be correctly programmed. However, it is possible those links contain typos or other mistakes. By discovering active but erroneous hyperlinks, a malicious actor can spoof awebsite or service, impersonating the expected content and phishing private information. In typosquatting, misspellings of common domains are registered to exploit errors when users mistype a web address. Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these hijackable hyperlinks exist in large quantities with the potential to generate substantial traffic. Analyzing largescale crawls of the web using high-performance computing, we show the web currently contains active links to more than 572 000 dot-com domains that have never been registered, what we term phantom domains. Registering 51 of these, we see 88% of phantom domains exceeding the traffic of a control domain, with up to 10 times more visits. Our analysis shows that these links exist due to 17 common publisher error modes, with the phantom domains they point to free for anyone to purchase and exploit for under $20, representing a low barrier to entry for potential attackers.

The energy industry is undergoing significant transformations as it strives to achieve net-zero emissions and future-proof its infrastructure, where every participant in the power grid has the potential to both consume and produce energy resources. Federated learning -- which enables multiple participants to collaboratively train a model without aggregating the training data -- becomes a viable technology. However, the global model parameters that have to be shared for optimization are still susceptible to training data leakage. In this work, we propose confined gradient descent (CGD) that enhances the privacy of federated learning by eliminating the sharing of global model parameters. CGD exploits the fact that a gradient descent optimization can start with a set of discrete points and converges to another set in the neighborhood of the global minimum of the objective function. As such, each participant can independently initiate its own private global model~(referred to as the confined model ), and collaboratively learn it towards the optimum. The updates to their own models are worked out in a secure collaborative way during the training process.In such a manner, CGD retains the ability of learning from distributed data but greatly diminishes information sharing. Such a strategy also allows the proprietary confined models to adapt to the heterogeneity in federated learning, providing inherent benefits of fairness. We theoretically and empirically demonstrate that decentralized CGD øne provides a stronger differential privacy (DP) protection; \two is robust against the state-of-the-art poisoning privacy attacks; þree results in bounded fairness guarantee among participants; and \four provides high test accuracy (comparable with centralized learning) with a bounded convergence rate over four real-world datasets.

The rapid progress in the field of IoT and its wide-ranging applications emphasize the criticality of robust security measures for effectively sharing, storing, and managing sensitive data generated by IoT devices. Regulations such as the Consumer Data Rights (CDR) highlight the need for the seamless sharing of sensitive data with authorized third parties while ensuring confidentiality and privacy. To enable such secure sharing, a data storage and sharing scheme should fulfill the following core requirements: (a) support multi-client data sharing settings, allowing IoT data owners to authorize multiple clients; (b) a dynamic storage environment permitting IoT owners to add or remove files with minimal privacy leak; (c) decentralized storage for distributing data across servers or Cloud Service Providers (CSPs) for greater security; and (d) efficient privilege revocation mechanism which incurs less computation and communication overhead. To address these requirements, we have proposed a novel keyword search scheme using computationally lightweight cryptographic primitives. Our scheme empowers IoT data owners to securely share, store and manage encrypted data in the CSPs, providing better security and privacy. We have provided formal security proof for our scheme as well as validated its efficiency via extensive experiments on the Docker platform. On a database of 12 million keyword/document pairs (with 105 documents and 103 keywords), our scheme took about 18 ms to return all matched documents.

Abstract Named Data Networking (NDN) is an emerging network architecture. An important characteristic of NDN is its in-network cache, which enables Data packets to be available from multiple locations on the Internet. Hence the enforcement of access control mechanisms becomes even more critical in the NDN. This paper proposes a novel access control scheme referred to as Role-Based Encryption for NDN (NDN-RBE), which uses a broadcast encryption mechanism to achieve secure data access control. Our scheme uses the role inheritance property of the traditional Role-Based Access Control (RBAC) model to achieve efficient data access control over hierarchical content. This makes our scheme particularly suitable for large-scale real-world content-centric services like Netflix. Our scheme also supports additional design features such as anonymous signature-based authentication, batch signature verification and two types of privilege revocations. In addition, our formal security analysis demonstrates that our scheme is provably secure against Chosen Plaintext Attacks. Our performance and functionality comparison show that our scheme outperforms other notable existing works in terms of security, functionality, computation, communication and storage overhead. Furthermore, our experimental results show an improvement in content delivery time of the order of 15 percent compared with the other closely related works.

For businesses looking to outsource their data to remote servers, cloud-based data storage is a popular choice. It is popular due to its flexibility, cost-effectiveness, and widespread availability. However, ensuring the confidentiality of data is a critical challenge that must be addressed. As a response to this issue, searchable encryption techniques have been developed. These techniques enable search queries to be performed on encrypted data while still keeping the plaintext confidential. While most existing symmetric searchable encryption schemes are designed for one-dimensional data records or document-keyword inverted indices, this paper introduces MDRSSE, a novel symmetric searchable encryption scheme specifically tailored for multi-dimensional range search. MDRSSE stands out as one of the pioneering SSE schemes to support multi-dimensional range search efficiently, without incurring undetermined additional communication or computation costs. By employing a single round of communication between the client and server, MDRSSE enables an honest-but-curious server to respond to multi-dimensional range queries without gaining knowledge of the data records or revealing the search query. Notably, MDRSSE boasts the lowest overall search complexity compared to existing state-of-the-art symmetric searchable encryption schemes designed for multi-dimensional range search. Extensive experimental tests were conducted to validate the robustness and practicality of our proposed scheme. The results demonstrate that, for a dataset consisting of 100K records with 12 dimensions (with each leaf node holding 500 records), it takes only 2.2 seconds to generate the encrypted dataset, and the overall setup phase completes within 2.5 seconds. Furthermore, for a range query encompassing 50 nodes, the search time is less than 2 ms and 3 ms for the client and server, respectively. MDRSSE achieves semantic security under the IND-CPA assumption, all without requiring additional storage size at the server.

While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research.

Federated learning (FL) is a distributed, privacy-preserving learning paradigm where a joint model is trained on private data stored on client devices. Data owners (clients) train models locally and then submit them to an aggregation server for incorporation into the joint model. Malicious clients can apply training time attacks, e.g., backdoor attacks, by submitting maliciously trained models. Prior work has shown that Differential Privacy (DP) can provide certified robustness to backdoor attacks; however, there are limited studies regarding DP parameter selection as a function of the model architecture. In this work, we show empirically that larger models (i.e., with more parameters) require stronger DP parameter settings to mitigate backdoor attacks. Furthermore, we present a framework that alters the FL training algorithm to preserve certified accuracy round-by-round and show empirically that it is superior to a model trainer selecting DP parameters ahead of time before training begins and with incomplete information about the attacker. Although tools from DP are used in our proposed framework, it is focused on backdoor attack mitigation and does not provide privacy guarantees.

Authentication and cryptography are critical security functions and, thus, are very often included as part of code. These functions require using credentials, such as passwords, security tokens, and cryptographic keys. However, developers often incorrectly implement/use credentials in their code because of a lack of secure coding skills. This paper analyzes open-source projects concerning the correct use of security credentials. We developed a semantic-rich, language-independent analysis approach for analyzing many projects automatically. We implemented a detection tool, SEAGULL, to automatically check open-source projects based on string literal and code structure information. Instead of analyzing the entire project code, which might result in path explosion when constructing data and control dependencies, SEAGULL pinpoints all literal constants to identify credential candidates and then analyzes the code snippets correlated to these candidates. SEAGULL accurately identifies the leaked credentials by obtaining semantic and syntax information about the code. We applied SEAGULL to 377 open-source projects. SEAGULL successfully reported 19 real-world credential leakages out of those projects. Our analysis shows that some developers protected or erased the credentials in the current project versions, but previously used credentials can still be extracted from the project’s historical versions. Although the implementations of credential leakages seem to be fixed in the current projects, attackers could successfully log into accounts if developers keep using the same credentials as before. Additionally, we found that such credential leakages still affect some projects. By exploiting leaked credentials, attackers can log into particular accounts.

Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking various face accessories into consideration, like glasses, hats, and masks. In the COVID-19 pandemic era, wearing face masks is one of the most effective ways to defend against the novel coronavirus. However, DNNs are known to be vulnerable to adversarial examples with a small but elaborated perturbation. Thus, a facial mask with adversarial perturbations may pose a great threat to the widely used deep learning-based FR models. In this paper, we consider a challenging adversarial setting: targeted attack against FR models. We propose a new stealthy physical masked FR attack via adversarial style optimization. Specifically, we train an adversarial style mask generator that hides adversarial perturbations inside style masks. Moreover, to ameliorate the phenomenon of sub-optimization with one fixed style, we propose to discover the optimal style given a target through style optimization in a continuous relaxation manner. We simultaneously optimize the generator and the style selection for generating strong and stealthy adversarial style masks. We evaluated the effectiveness and transferability of our proposed method via extensive white-box and black-box digital experiments. Furthermore, we also conducted physical attack experiments against local FR models and online platforms.

Forgery of facial images and videos has increased the concern about digital security. It has led to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We believe creating a forgery detection capable of handling data compressed with unknown levels is important. To enhance the performance of such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, our algorithm measures the pair-wise similarity within data as the relations, ensuring that relationships between weakly and strongly compressed data remain consistent. This enhances the discriminative power for detecting highly compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video-level contrastive learning for weak compressed data, which forces the model to produce similar representations within the same video and far from the negative samples. The experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting clean data.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years-particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

The susceptibility of deep neural networks (DNNs) to adversarial intrusions, exemplified by adversarial examples, is well-documented. Conventional attacks implement unstructured, pixel-wise perturbations to mislead classifiers, which often results in a noticeable departure from natural samples and lacks human-perceptible interpretability. In this work, we present an adversarial attack strategy that implements fine-granularity, semantic-meaning-oriented structural perturbations. Our proposed methodology manipulates the semantic attributes of images through the use of disentangled latent codes. We engineer adversarial perturbations by manipulating either a single latent code or a combination thereof. To this end, we propose two unsupervised semantic manipulation strategies: one based on vector-disentangled representation and the other on feature map-disentangled representation, taking into consideration the complexity of the latent codes and the smoothness of the reconstructed images. Our empirical evaluations, conducted extensively on real-world image data, showcase the potency of our attacks, particularly against black-box classifiers. Furthermore, we establish the existence of a universal semantic adversarial example that is agnostic to specific images.

Security Operations Centres (SOCs) play a pivotal role in defending organisations against evolving cyber threats. They function as central hubs for detecting, analysing, and responding promptly to cyber incidents with the primary objective of ensuring the confidentiality, integrity, and availability of digital assets. However, they struggle against the growing problem of alert fatigue, where the sheer volume of alerts overwhelms SOC analysts and raises the risk of overlooking critical threats. In recent times, there has been a growing call for human-AI teaming, wherein humans and AI collaborate with each other, leveraging their complementary strengths and compensating for their weaknesses. The rapid advances in AI and the growing integration of AI-enabled tools and technologies within SOCs give rise to a compelling argument for the implementation of human-AI teaming within the SOC environment. Therefore, in this article, we present our vision for human-AI teaming to address the problem of alert fatigue in the SOC. We propose the 𝒜 2 𝒞 Framework, which enables flexible and dynamic decision making by allowing seamless transitions between automated, augmented, and collaborative modes of operation. Our framework allows AI-powered automation for routine alerts, AI-driven augmentation for expedited expert decision making, and collaborative exploration for tackling complex, novel threats. By implementing and operationalising 𝒜 2 𝒞, SOCs can significantly reduce alert fatigue while empowering analysts to efficiently and effectively respond to security incidents.

The emerging threat landscape causes continuous change in the Incident Response Process (IRP) and security tools of security orchestration platforms (SOAR). Users of such platforms often struggle to adapt to these changes because they are addressed in an ad-hoc manner through a complex architecture. The complex design of the SOAR can be hidden behind an easy-to-use user interface. This article introduces a Declarative API (DAPI)-driven Orchestration approach, DecOr, that alleviates the need for security teams’ detailed understanding of the libraries and plugins to address the changes of a SOAR. DecOr comprises 1) three sets of dAPIs to encapsulate the activities of security orchestration and 2) a semantic framework to support the design and generation of dAPIs from task descriptions, leveraging natural language processing techniques. The dAPIs are mapped with an ontological knowledge base to execute IRPs. We experimentally evaluate the effectiveness and efficiency of DecOr based on 147 task and dAPI pairs, curated from real-world playbooks. We show the end-to-end process from identifying dAPIs to executing 48 IRPs with seven security tools. The evaluation results show, DecOr accurately generates dAPIs in near real-time, with precision and recall values over 80% and successfully executes changing IRPs 93% of the time.

Developers and manufacturers provide configurable control parameters for flight control programs to support various environments and missions, along with suggested ranges for these parameters to ensure flight safety. However, this flexible mechanism can also introduce a vulnerability known as range specification bugs. The vulnerability originates from the evidence that certain combinations of parameter values may affect the drone's physical stability even though its parameters are within the suggested range. The paper introduces a novel system calledicsearcher, designed to identify incorrect configurations or unreasonable combinations of parameters and suggest more reasonable ranges for these parameters.icsearcherapplies a metaheuristic search algorithm to find configurations with a high probability of driving the drone into unstable states. In particular,icsearcheradopts a machine learning-based predictor to assist the searcher in evaluating the fitness of configuration. Finally, leveraging searched incorrect configurations,icsearchercan summarize the feasible ranges through multi-objective optimization.icsearcherapplies a predictor to guide the search, which eliminates the need for realistic/simulation executions when evaluating configurations and further promotes search efficiency. We have carried out experimental evaluations oficsearcherin different control programs. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over$94\%$leads to unstable states.

Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking techniques that embed confidential data into DNNs have been used to protect ownership, but these can degrade model performance and are vulnerable to watermark removal attacks. Recently, DeepJudge was introduced as an alternative approach to measuring the similarity between a suspect and a victim model. While DeepJudge shows promise in addressing the shortcomings of watermarking, it primarily addresses situations where the suspect model copies the victim’s architecture. In this study, we introduce DeepTaster, a novel DNN fingerprinting technique, to address scenarios where a victim’s data is unlawfully used to build a suspect model. DeepTaster can effectively identify such DNN model theft attacks, even when the suspect model’s architecture deviates from the victim’s. To accomplish this, DeepTaster generates adversarial images with perturbations, transforms them into the Fourier frequency domain, and uses these transformed images to identify the dataset used in a suspect model. The underlying premise is that adversarial images can capture the unique characteristics of DNNs built with a specific dataset. To demonstrate the effectiveness of DeepTaster, we evaluated the effectiveness of DeepTaster by assessing its detection accuracy on three datasets (CIFAR10, MNIST, and Tiny-ImageNet) across three model architectures (ResNet18, VGG16, and DenseNet161). We conducted experiments under various attack scenarios, including transfer learning, pruning, fine-tuning, and data augmentation. Specifically, in the Multi-Architecture Attack scenario, DeepTaster was able to identify all the stolen cases across all datasets, while DeepJudge failed to detect any of the cases.

Privacy-preserving image generation has been important for segments such as medical domains that have sensitive and limited data. The benefits of guaranteed privacy come at the costs of generated images' quality and utility due to the privacy budget constraints. The utility is currently measured by the gen2real accuracy (g2r%), i.e., the accuracy on real data of a downstream classifier trained using generated data. However, apart from this standard utility, we identify the “reversed utility” as another crucial aspect, which computes the accuracy on generated data of a classifier trained using real data, dubbed as real2gen accuracy (r2g%). Jointly considering these two views of utility, the standard and the reversed, could help the generation model better improve transferability between fake and real data. Therefore, we propose a novel private image generation method that incorporates a dual-purpose auxiliary classifier, which alternates between learning from real data and fake data, into the training of differentially private GANs. Additionally, our deliberate training strategies such as sequential training contributes to accelerating the generator's convergence and further boosting the performance upon exhausting the privacy budget. Our results achieve new state-of-the-arts over all metrics on three benchmarks: MNIST, Fashion-MNIST, and CelebA.

Emulation-based fuzzers enable testing binaries without source code and facilitate testing embedded applications where automated execution on the target hardware architecture is difficult and slow. The instrumentation techniques added to extract feedback and guide input mutations towards generating effective test cases is at the core of modern fuzzers. But, modern emulation-based fuzzers have evolved by re-purposing general-purpose emulators; consequently, developing and integrating fuzzing techniques, such as instrumentation methods, is difficult and often added in an ad-hoc manner, specific to an instruction set architecture (ISA). This limits state-of-the-art fuzzing techniques to a few ISAs such as x86/x86-64 or ARM/AArch64; a significant problem for firmware fuzzing of diverse ISAs.

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipulation. In this work, we propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors when confronted with adversarial attacks. The framework introduces the concept of Accrued Malicious Magnitude (AMM) to identify which malware features could be manipulated to maximize the likelihood of evading detection. We then use this framework to test several state-of-the-art malware detectors' ability to detect manipulated malware. We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i.e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i.e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.

Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model parameters). However, private verification approaches do not allow model users to verify the model at run-time. Instead, they must trust the service provider, who may tamper with the verification results. In contrast, a public verification approach that considers the possibility of dishonest service providers can benefit a wider range of users. In this paper, we propose PublicCheck, a practical public integrity verification solution for services of run-time deep models. PublicCheck considers dishonest service providers, and overcomes public verification challenges of being lightweight, providing anti-counterfeiting protection, and having fingerprinting samples that appear smooth. To capture and fingerprint the inherent prediction behaviors of a run-time model, PublicCheck generates smoothly transformed and augmented encysted samples that are enclosed around the model's decision boundary while ensuring that the verification queries are indistinguishable from normal queries. PublicCheck is also applicable when knowledge of the target model is limited (e.g., with no knowledge of gradients or model parameters). A thorough evaluation of PublicCheck demonstrates the strong capability for model integrity breach detection (100% detection accuracy with less than 10 black-box API queries) against various model integrity attacks and model compression attacks. PublicCheck also demonstrates the smooth appearance, feasibility, and efficiency of generating a plethora of encysted samples for fingerprinting.

Object detection is the foundation of various critical computer-vision tasks such as segmentation, object tracking, and event detection, which can be deployed on pervasive Internet of Things (IoT) and edge devices. A large amount of data is often required to train an object detector with satisfactory accuracy. However, due to the intensive workforce involved with collecting and annotating large datasets, data curation task is often outsourced to a third party (e.g., Amazon Mechanical Turk) or volunteers. This work reveals severe vulnerabilities in this data curation pipeline. We propose TransCAB, the first work to craft clean-annotated images to stealthily implant the backdoor into the object detectors later trained on them by the data curator/user even when the data curator can manually audit the images and fully controls the training process. Existing clean-label poisoned images are only shown in classification tasks but not non-classification tasks, in particular, object detection due to unique challenges faced, generally owing to the complexity of having multiple objects within each frame (image), including the victim and non-victim objects. Furthermore, we demonstrate that the backdoor effect of both cloaking and misclassification are robustly achieved in the wild when the backdoor is activated with inconspicuously natural physical object as trigger (i.e., T-shirt). The efficacy of our TransCAB is ensured by constructively i) applying the image-camouflage attack that abuses the image-scaling function widely used by the deep learning framework (i.e., PyTorch), ii) incorporating the devised clean image replica technique, and iii) combining identified poison data selection criteria given constrained attacking budget. Extensive experi-ments on YOLOv3, YOLOv4, CenterNet, and Faster R-CNN affirm that TransCAB exhibits more than 90% attack success rate under various real-world scenes even when a very small (i.e., 0.14%) dataset fraction is poisoned. In addition, the small set of poisoned images crafted on one detector (i.e., YOLOv3) can be effectively transferred to insert a backdoor on another detector (i.e., CenterNet). A comprehensive video demo is at https://youtu.be/MA7L_LpXkp4, where a poison rate of merely 0.14% is set for YOLOv4 cloaking backdoor and Faster R-CNN misclassification backdoor. Our collected dataset with T-shirt as a natural trigger (about 11,350 frames in total) is open to the public at https://github.com/inconstance/T-shirt-natural-backdoor-dataset, which is the first relatively large-scale natural trigger backdoor dataset.

The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world’s most marginalized children. However, while legislatures around the world have enacted regulations to protect children’s online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. We investigate 20195 mobile apps from the Google Play store that are designed particularly for children (Family apps) or include children in their target user groups (Normal apps). Using both static and dynamic analysis, we find that 4.47% of Family apps request location permissions, even though collecting location information from children is forbidden by the Play store, and 81.25% of Family apps use trackers (which are not allowed in children’s apps). Even major developers with 40+ kids apps on the Play store use ad trackers. Furthermore, we find that most permission request notifications are not well designed for children, and 19.25% apps have inconsistent content age ratings across the different protection authorities. Our findings suggest that, despite significant attention to children’s privacy, a large gap between regulatory provisions, app store policies, and actual development practices exist. Our research sheds light for government policymakers, app stores, and developers.

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that poses a significant threat to federated learning (FL). An adversary conducts data poisoning (i.e., performing adversarial manipulations on training examples) to extract membership information by exploiting the changes in loss resulting from data poisoning. The PMIA significantly exacerbates the traditional poisoning attack that is primarily focused on model corruption. However, there has been a lack of a comprehensive systematic study that thoroughly investigates this topic. In this work, we conduct a benchmark evaluation to assess the performance of PMIA against the Byzantine-robust FL setting that is specifically designed to mitigate poisoning attacks. We find that all existing coordinate-wise averaging mechanisms fail to defend against the PMIA, while the detect-then-drop strategy was proven to be effective in most cases, implying that the poison injection is memorized and the poisonous effect rarely dissipates. Inspired by this observation, we propose AgrEvader, a PMIA that maximizes the adversarial impact on the victim samples while circumventing the detection by Byzantine-robust mechanisms. AgrEvader significantly outperforms existing PMIAs. For instance, AgrEvader achieved a high attack accuracy of between 72.78% (on CIFAR-10) to 97.80% (on Texas100), which is an average accuracy increase of 13.89% compared to the strongest PMIA reported in the literature. We evaluated AgrEvader on five datasets across different domains, against a comprehensive list of threat models, which included black-box, gray-box and white-box models for targeted and non-targeted scenarios. AgrEvader demonstrated consistent high accuracy across all settings tested. The code is available at: https://github.com/PrivSecML/AgrEvader.

Businesses face a range of cyber risks, both external threats and internal vulnerabilities that continue to evolve over time. As cyber attacks continue to increase in complexity and sophistication, more organisations will experience them. For this reason, it is important that organisations seek timely consultancy from cyber professionals so that they can respond to and recover from cyber attacks as quickly as possible. However, huge surges in cyber attacks have long left cyber professionals short of what is required to cover the security needs. This problem is getting worse when an increasing number of people choose to work from home during the pandemic because this situation usually yields extra communication cost.

Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm,TStream, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge.TStreamis based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (akamicro-clusters) in the memory. We also propose a novel efficient index calledshared dependency tree(akaSD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships inTStreamand thereby improves the overall efficiency. We conduct extensive experiments on five real datasets.TStreamoutperforms the existing state-of-the-art solutions based onMStream,MStreamF,EDMStream,OSGM, andEStream, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient thanMStream,MStreamF,OSGM, andEStream, in terms of response time and throughput.

Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families’ malware may remain undetected until the classifier gains specialized knowledge. The most existing ML-based methods will take a long time to learn new malware families in the latest malware family landscape. Existing ML-based Android malware detection and classification methods struggle with the fast evolution of the malware landscape, particularly in terms of the emergence of zero-day malware families and limited representation of single-view features. In this article, a new multiview feature intelligence (MFI) framework is developed to learn the representation of a targeted capability from known malware families for recognizing unknown and evolving malware with the same capability. The new framework performs reverse engineering to extract multiview heterogeneous features, including semantic string features, API call graph features, and smali opcode sequential features. It can learn the representation of a targeted capability from known malware families through a series of processes of feature analysis, selection, aggregation, and encoding, to detect unknown Android malware with shared target capability. We create a new dataset with ground-truth information regarding capability. Many experiments are conducted on the new dataset to evaluate the performance and effectiveness of the new method. The results demonstrate that the new method outperforms three state-of-the-art methods, including: 1) Drebin; 2) MaMaDroid; and 3)$N$-opcode, when detecting unknown Android malware with targeted capabilities.

Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Internet of Things devices for which cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the small, but unpredictable variations in fingerprint reproductions from the same device due to measurement noise. Our study formulates a novel and pragmatic approach to achieve highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints is intrinsically highly reliable. We derive formal performance bounds to support practitioners to easily adopt our methods for applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. Together with extensive simulations, we have employed 119 chips from five different manufacturers for extensive experimental validations. Our results, including an end-to-end implementation demonstration with a low-cost wearable Bluetooth inertial sensor capable of on-demand and runtime key generation, show that key generators with failure rates less than$10^{-6}$can be efficiently obtained with noise-tolerant fingerprints with a single fingerprint snapshot to support ease-of-enrollment.

Genome-wide analysis has demonstrated both health and social benefits. However, large scale sharing of such data may reveal sensitive information about individuals. One of the emerging challenges is identity tracing attack that exploits correlations among genomic data to reveal the identity of DNA samples. In this paper, we first demonstrate that the adversary can narrow down the sample's identity by detecting his/her genetic relatives and quantify such privacy threat by employing a Shannon entropy-based measurement. For example, we exemplify that when the dataset size reaches 30% of the population, for any target from that population, the uncertainty of the target's identity is reduced to merely 2.3 bits of entropy (i.e., the identity is pinned down within 5 people). Direct application of existing approaches such as differential privacy (DP), secure multiparty computation (MPC) and homomorphic encryption (HE) may not be applicable to this challenge in genome-wide analysis because of the compromise on utility (i.e., accuracy or efficiency). Towards addressing this challenge, this paper proposes a framework named$\upsilon$Fragto facilitate privacy-preserving data sharing and computation in genome-wide analysis.$\upsilon$Fragmitigates privacy risks by using a vertical fragmentation to disrupt the genetic architecture on which the adversary relies for identity tracing without sacrificing the capability of genome-wide analysis. We theoretically prove that it preserves the correctness of the primitive functionalities and algorithms ranging from basic summary statistics to advanced neural networks. Our experiments demonstrate that$\upsilon$Fragoutperforms secure multiparty computation (MPC) and homomorphic encryption (HE) protocols, with a speedup of more than 221x for training neural networks, and also traditional non-private algorithms and a state-of-the-art noise-based differential privacy (DP) solution in most settings.

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to hammer rows can induce bit flips in neighboringvictim rows. Rowhammer attacks have enabled privilege escalation, sandbox escape, cryptographic key disclosures, etc. A key requirement ofallexisting rowhammer attacks is that an attacker must have access to at least part of an exploitable hammer row. We term such rowhammer attacks as Explicit Hammer. Recently, several proposals leverage the spatial proximity between the accessed hammer rows and the location of the victim rows for a defense against rowhammer. These all aim to deny the attacker's permission to access hammer rows near sensitive data, thus defeating explicit hammer-based attacks. In this paper, we question the core assumption underlying these defenses. We present Implicit Hammer, a confused-deputy attack that causes accesses to hammer rows that the attacker is not allowed to access. It is a paradigm shift in rowhammer attacks since it crosses privilege boundary to stealthily rowhammer an inaccessible row by implicit DRAM accesses. Such accesses are achieved by abusing inherent features of modern hardware and/or software. We propose a generic model to rigorously formalize the necessary conditions to initiate implicit hammer and explicit hammer, respectively. Compared to explicit hammer, implicit hammer can defeat the advanced software-only defenses, stealthy in hiding itself and hard to be mitigated. To demonstrate the practicality of implicit hammer, we have created two implicit hammer's instances, called PThammer and SyscallHammer.

Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design, implement, and evaluate a new system that allows highly efficient outsourcing of decision tree inference. Our system significantly improves upon prior art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider. We first present a new scheme which securely shifts most of the processing of the model provider to the cloud, resulting in a substantial reduction on the model provider's performance complexities. We further devise a scheme which substantially optimizes the performance for secure decision tree inference at the cloud, particularly the communication round complexities. The synergy of these techniques allows our new system to achieve up to$8 \times$better overall online end-to-end secure inference latency at the cloud side over realistic WAN environment, as well as bring the model provider up to$19 \times$savings in communication and$18 \times$savings in computation.

Cloud storage systems have seen a growing number of clients due to the fact that more and more businesses and governments are shifting away from in-house data servers and seeking cost-effective and ease-of-access solutions. However, the security of cloud storage is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB’s padding strategies ensure that the access pattern to the database is obfuscated all the time. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud.

Blockchain-based Zero Trust Cybersecurity in the Internet of Things 1 INTRODUCTIONThe Internet of Things (IoT) connects a massive number of smart devices to the Internet, in which all data, applications, devices, and users require connectivity, security, and trust.Traditional security approaches assume that all participants within the network perimeter are trustworthy.However, in IoT environment data, applications, devices, and users are gradually moving outside the traditional trusted defence perimeter and have become a source of security risks.Unlike traditional security approaches, which are initially designed for the optimum protection and only act if a process is malicious, the zero-trust security framework upholds the "verify and never trust" principle.Zero trust-based approaches assume that everything within the system is untrustworthy and needs to be verified to prevent threats.Meanwhile, the blockchain technology shows promises on cyber security and several blockchain security mechanisms have been developed, including access management, user authentication, and transaction security.Due to its prowess in enhancing cyber security, blockchain can provide zero trust security framework with highly accessible and transparent security mechanisms via a visible blockchain, in which all transactions are visible to restricted operators.Zero-trust models can be secured further by a blockchain due to its sheer immutable nature and blockchain technology is expected to recognise them, authenticate their trust, and allow them access.Blockchain-enabled zero trust security can detect suspicious online transaction, isolate connection, and restrict access to the user.This special issue received in total 37 high-quality submissions.Per journal policy, it was ensured that handling editors did not have any potential conflict of interest with authors of submitted papers.All submitted papers were reviewed by at least three independent potential referees.The papers were evaluated for their rigor and quality, and also for their relevance to the theme of our special issue.After evaluating the overall scores, seven papers were selected by the guest editors and approved by the Editor-in-Chief for inclusion in this special issue.We will now briefly introduce the accepted papers. THE PAPERSThe paper entitled "Three-tier storage framework based on TBchain and IPFS for protecting IoT security and privacy" by authors Li et al. proposed to use a three-tier blockchain to split

Multi-turn response selection is a key issue in retrieval-based chatbots and has attracted considerable attention in the NLP (Natural Language processing) field. So far, researchers have developed many solutions that can select appropriate responses for multi-turn conversations. However, these works are still suffering from the semantic mismatch problem when responses and context share similar words with different meanings. In this article, we propose a novel chatbot model based on Semantic Awareness Matching, called SAM. SAM can capture both similarity and semantic features in the context by a two-layer matching network. Appropriate responses are selected according to the matching probability made through the aggregation of the two feature types. In the evaluation, we pick 4 widely used datasets and compare SAM’s performance to that of 12 other models. Experiment results show that SAM achieves substantial improvements, with up to 1.5% R 10 @1 on Ubuntu Dialogue Corpus V2, 0.5% R 10 @1 on Douban Conversation Corpus, and 1.3% R 10 @1 on E-commerce Corpus.

Low-cost transient resources such as Amazon's Elastic Compute Cloud (EC2) Spot instances can be opportunistically leveraged to reduce the ongoing costs of cloud applications. However, they are susceptible to unilateral revocations by the vendor making them a risky proposition for long-running applications with strict performance requirements. It is challenging to effectively balance the cost savings that transient resources provide with the associated revocation risk which, if realised, can impact application performance. To address this challenge, we propose an approach for risk-aware cloud cost optimization that is inspired by the concept ofportfolio diversification.Contract diversificationmitigates the revocation risk by procuring the required compute capacity as a mixed portfolio of transient and non-transient resources.Resource diversificationfurther diversifies the risk by using multiple transient resource types. Using our approach, consumers can leverage contract and resource diversification to proactively (re-)configure their application's resource portfolio to handle workload and resource price fluctuations while minimizing ongoing cost and keeping ongoing revocation risk within tolerable limits. Simulative evaluation using three real-world workload traces and Amazon's EC2 offerings demonstrate that our proposed approach can achieve meaningful cost savings compared to the baseline costs, while significantly reducing the portfolio's exposure to revocation risk.

The healthcare Internet of Things (IoT) is rapidly becoming an invaluable tool in the healthcare industry. However, sharing data in healthcare IoT raises many security and privacy concerns, such as how to ensure data integrity, source authentication, and data privacy. Redactable signature schemes (${{\sf RSS}}$s) could be a feasible solution to address this question because it allows a signature holder to independently delete the privacy-sensitive part of the authenticated data without invalidating the respective signature. This flexible data sharing mechanism not only protects data privacy but also saves bandwidth. However, the state-of-the-art${{\sf RSS}}$s suffer from either the costly public key management problem or the secret key escrow problem. Another drawback of these schemes lies in their computation and communication overheads and hence are quite expensive for constrained devices. To address these challenging issues, in this work, we first propose the notion of certificateless${{\sf RSS}}$. We then provide an efficient instantiation of our scheme and prove its security under cryptographic assumptions. Our construction supports batch verification and redaction control, which further saves bandwidth and enhances the security of shared data by preventing the dishonest holder from arbitrarily editing data. Moreover, the comparison analysis of theory and experiment with more recent works shows the practicability of our design.

Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due to their strong theoretical privacy guarantees, differential private publication mechanisms receive much attention. However, the large amount of noise required to achieve differential privacy yields structural differences, e.g., ship trajectories passing over land. We propose a deep learning-based Reconstruction Attack on Protected Trajectories (RAoPT), that leverages the mentioned differences to partly reconstruct the original trajectory from a differential private release. The evaluation shows that our RAoPT model can reduce the Euclidean and Hausdorff distances between the released and original trajectories by over 68 % on two real-world datasets under protection with ε ≤ 1. In this setting, the attack increases the average Jaccard index of the trajectories’ convex hulls, representing a user’s activity space, by over 180 %. Trained on the GeoLife dataset, the model still reduces the Euclidean and Hausdorff distances by over 60 % for T-Drive trajectories protected with a state-of-the-art mechanism (ε = 0.1). This work highlights shortcomings of current trajectory publication mechanisms, and thus motivates further research on privacy-preserving publication schemes.

The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these models in one domain to other related domains, and the closeness of natural languages to high-level programming languages, such as C/C++, this work studies how to leverage (large) transformer-based language models in detecting software vulnerabilities and how good are these models for vulnerability detection tasks. In this regard, firstly, we present a systematic (cohesive) framework that details source code translation, model preparation, and inference. Then, we perform an empirical analysis of software vulnerability datasets of C/C++ source codes having multiple vulnerabilities corresponding to the library function call, pointer usage, array usage, and arithmetic expression. Our empirical results demonstrate the good performance of the language models in vulnerability detection. Moreover, these language models have better performance metrics, such as F1-score, than the contemporary models, namely bidirectional long short term memory and bidirectional gated recurrent unit. Experimenting with the language models is always challenging due to the requirement of computing resources, platforms, libraries, and dependencies. Thus, this paper also analyses the popular platforms to efficiently fine-tune these models and present recommendations while choosing the platforms for our framework.

Training machine learning algorithms on sensitive, illegal to possess, and psychologically harmful data is challenging because researchers have to do training without handling the data. Moreover, the nature of the data imposes strict control, monitoring, and examination of all the activities involved, including communication, execution, and release of algorithms, datasets, outputs, and results. In this regard, this work proposes a new multi-zoned framework called MaLFraDA. MaLFraDA has soft air gaps between its zones to isolate and control communication in and out of the framework. Besides, it includes (i) a vetter to investigate and approve incoming model/algorithm, and outgoing information, (ii) encrypted data vaults, and (iii) airlock instances for secure execution/computation. MaLFraDA, with an extension, runs popular distributed machine learning algorithms such as federated and split learning using multiple data custodians.

In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would become a real threat to both celebrities and ordinary people. Precautions such as adding perturbations to the source inputs will make DeepFake results look distorted from the perspective of human eyes. However, previous method doesn't explore whether the disrupted images can still spoof DeepFake detectors. This is critical for many applications where DeepFake detectors are used to discriminate between DeepFake data and real data due to the huge cost of examining a large amount of data manually. We argue that the detectors do not share a similar perspective as human eyes, which might still be spoofed by the disrupted data. Besides, the existing disruption methods rely on iteration-based perturbation generation algorithms, which is time-consuming. In this paper, we propose a novel DeepFake disruption algorithm called “DeepFake Disrupter”. By training a perturbation generator, we can add the human-imperceptible perturbations to source images that need to be protected without any backpropagation update. The DeepFake results of these protected source inputs would not only look unrealistic by the human eye but also can be distinguished by DeepFake detectors easily. For example, experimental results show that by adding our trained perturbations, fake images generated by StarGAN [5] can result in a 10 ∼ 20% increase in F1-score evaluated by various DeepFake detectors.

Cyber deception is the practice of deliberately introducing fake or misleading artefacts into cyber systems. It is emerging as a promising approach to defending networks and systems against attackers and data thieves. However, despite being relatively cheap to deploy [1], the generation of realistic content at scale is very costly when it is hand-crafted. With recent improvements in Machine Learning, we now have the opportunity to bring scale and automation to the creation of realistic and enticing simulated content. In this work, we propose a framework to automate the generation of email and instant messaging-style group communications at scale. Such messaging platforms within organisations contain a lot of valuable information inside private communications and document attachments, making them an enticing target for an adversary. The presence of an active messaging platform also enhances the realism of a deceptive network simulation, contributing both traffic and message artefacts. We address two key aspects of simulating this type of system: modelling when and with whom participants communicate, and generating topical, multi-party text to populate simulated conversation threads. We present the LogNormMix-Net Temporal Point Process as an approach to the first of these, building upon the intensity-free modeling approach of Shchur et al. [2] to create a generative model for unicast and multi-cast communications. We demonstrate the use of fine-tuned, pretrained language models to generate convincing multi-party conversation threads. A live email server is simulated by uniting our LogNormMix-Net TPP (to generate the communication timestamp, sender and recipients) with the language model, which generates the contents of the multi-party email threads. We evaluate the generated content with respect to a number of realism-based properties, that encourage a model to learn to generate content that will engage the attention of an adversary to achieve a deception outcome. Our simulations run in real time, making them suitable for deployment in cyber deception as a honeypot in its own right, or as part of a larger deception environment.

Many Machine Learning (ML) based phishing detection algorithms are not adept to recognise "concept drift"; attackers introduce small changes in the statistical characteristics of their phishing attempts to successfully bypass detection. This leads to the classification problem of frequent false positives and false negatives, and a reliance on manual reporting of phishing by users. Profiler is a distributed phishing risk assessment tool that combines three email profiling dimensions: (1) threat level, (2) cognitive manipulation, and (3) email content type to detect email phishing. Unlike pure ML-based approaches, Profiler does not require large data sets to be effective and evaluations on real-world data sets show that it can be useful in conjunction with ML algorithms to mitigate the impact of concept drift.

Artificial intelligence has been investigated to conduct automatic predictions on financial time series such as stock. However, they faced two challenges. Firstly, the stock movement is affected by both technical fundamentals and external textual information. Secondly, resource data are often highly-noisy and heterogeneous, and prediction based on noisy data usually leads to significant errors. We propose a robust and accurate model (R-Net) that incorporates both technical information and qualitative sentiment derived from news reports for daily stock movement prediction in response to these challenges. A variety of enhancement strategies are adopted to improve the prediction model's robustness and accuracy. Specifically, a multimodal CNN and LSTM neural networks are applied to extract semantics from text and model complex temporal characteristics for stock market prediction. Further, based on the connection between the robustness of deep neural networks and differential privacy, we utilize provable noise injection and heterogeneous Gaussian mechanisms to enhance model robustness and accuracy. Experimental results on S&P 500 stocks demonstrate that our proposed R-Net, which integrates four enhancements, achieves 12.7% and 0.67% improvement in prediction accuracy for trends and price value prediction, respectively.

Abstract The edge authentication of graphs has been studied in the literature because graphs are one of the most widely used data organization structures. The majority of such schemes cannot be used to authenticate general directed graphs (GDGs); other schemes cannot be used for addressing either the issue of dynamic update or the issue of information leakage (such as the existence of nodes/edges and structural relationship of the graph). Also, all the existing schemes do not consider the forward security: if the signer’s secret key has been compromised, all previously generated signatures remain valid. This property provides high-level security protection for authentication schemes. To address these issues, in this work, we propose a forward-secure edge authentication scheme for GDGs. Observe that existing such schemes can only give a proof such that ‘there is an edge between nodes $u$ and $v$’. Our scheme, however, can directly give a proof such that ‘there is no edge between nodes $u$ and $v$’, which makes the function of edge authentication schemes more diverse. Moreover, our proposed scheme is proven to be secure against an adaptive chosen-message adversary in the random oracle model. To show its desirable performance, we analyze the computational costs of our scheme and compare it with other related schemes in terms of features.

Machine learning (ML) techniques are increasingly important in cybersecurity, as they can quickly analyse and identify different types of threats from millions of events. In spite of the increasing number of possible applications of ML, successful adoption of ML models in cybersecurity still highly relies on the explainability of those models that are used for making predictions. Explanations that support ML model outputs are crucial in cybersecurity-oriented ML applications because people need to get more information from the model than just binary output for analysis. The explainable models help ML developers solve the “trust” problem for a security application prediction in a faithful way: validating model behaviours, diagnosing misclassifications and sometimes automatically patching errors in the target models. Therefore, explainable ML for cybersecurity has become a necessary and important research branch. In this paper, we present the topic of explainable ML in cybersecurity through two general types of explanations: (1) ante hoc explanation, and (2) post hoc explanation, with their methodologies. We systematically review and categorise the state-of-the-art research, and provide comparative studies to help researchers find the optimal solutions to specific problems. We further list open issues in this field to facilitate future studies. This survey will benefit diverse groups of readers from both academia and industries, who want to effectively use ML to solve cybersecurity challenges.

Wireless body area networks (WBANs) technology nowadays has become a promising networking paradigm in the Internet of Things (IoT) as it can provide people with high quality of life and high level of medical service. In order to ensure the security and privacy of patients’ sensitive biomedical data and the efficiency of message processing across different devices, it is critical to provide a secure and lightweight authentication scheme for WBANs. In this article, we propose an extra lightweight authentication scheme for mobile-edge computing-enabled WBANs. Two different authentication phases based on the modular square roots technique are designed: one is the intra-BAN authentication between the sensor node and edge node (EN), and the other is the inter-BAN authentication between EN and application provider. The proposed scheme offers robust security by providing comprehensive security analysis. Performance is also evaluated in terms of computation, communication, and storage costs. The evaluation results demonstrate that the proposed scheme achieves a reduction of at least 90% in computation cost and at least 30% in communication cost when compared to four other related schemes.

Over the past decade, the Internet of Things (IoT) is widely adopted in various domains, including education, commerce, government, and healthcare. There are also many IoT-based applications drawn significant attentions in recent years. With the increasing numbers of the connected devices in the IoT system, one of the challenging tasks is to ensure devices’ authenticity, which allows users to have a high confidence in the decision. In addition, due to the heterogeneity of the IoT system and the resource-constrained devices, how to efficiently manage such system and guarantee the security and privacy for devices is concerned. In this article, we proposed a new blockchain-based authentication scheme to meet the challenges. Our proposed framework combines the blockchain technique and the modular square root algorithm to achieve an effective authentication process. Besides, we demonstrate the security and utility of the proposed scheme by providing the security analysis and the detailed experiment.

Wireless medical sensor networks (WMSNs) have aroused widespread attention in recent years with the development of Internet of Things (IoT) technology. WMSNs offer many new opportunities for healthcare professionals to monitor patients and patient self-monitoring. To overcome the resource (such as memory and power) limitations of sensors and attain data security of patients’ private medical information, researchers have designed plenty of work for securing WMSNs. For years, certificate-based aggregate signature (CBAS) schemes have been put forward for WMSNs to prevent patients’ sensitive medical data from being tampered with and damaged. In this work, we analyze the security flaws of a very recent CBAS scheme proposed by Vermaet al.(2021) by presenting two types of security attacks. We later propose a CBAS scheme with user anonymity protection for WMSNs and prove its security based on the standard cryptographic assumption. The performance comparison results from theory and experiment illustrate the practicality of our design.

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clients or end devices. However, their comparative training performance under real-world resource-restricted Internet of Things (IoT) device settings remains barely studied. This work provides empirical comparisons of FL and SL in real-world IoT settings regarding (i) learning performance with heterogeneous data distributions and (ii) on-device execution overhead. Our analyses in this work demonstrate that the learning performance of SL is better than FL under an imbalanced data distribution but worse than FL under an extreme non-IID data distribution. Recently, FL and SL are combined to form splitfed learning (SFL) to leverage each of their benefits (e.g., parallel training of FL and lightweight on-device computation requirement of SL). Our work considers FL, SL, and SFL, and mounts them on Raspberry Pi devices to evaluate their performance, including training time, communication overhead, power consumption, and memory usage with resource-restricted IoT devices. Besides evaluations, we apply two optimizations. First, we generalize SFL by carefully examining the possibility of a hybrid type of model training at the server-side. The generalized SFL merges sequential (dependent) and parallel (independent) processes of model training and thus is beneficial to a system with a large scale of IoT devices, specifically at the server-side operations. Second, we propose pragmatic techniques to substantially reduce the communication overhead by up to four times for the SL and (generalized) SFL.

This article demonstrates that nonmaximum suppression (NMS), which is commonly used in object detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this article, an adversarial example attack that triggers malfunctioning of NMS in OD models is proposed. The attack, namely, Daedalus, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications, such as autonomous vehicles and surveillance systems. The attack can be generalized to different OD models, such that the attack cripples various OD applications. Furthermore, a way of crafting robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reuse in real-world OD scenarios, Daedalus examples crafted based on an ensemble of substitutes can launch attacks without knowing the parameters of the victim models. The experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to 99.9% and reduces the mean average precision scores to 0, while maintaining a low cost of distortion on the original inputs. It also demonstrates that the attack can be practically launched against real-world OD systems via printed posters.

A physical unclonable function (PUF) generates hardware intrinsic volatile secrets by exploiting uncontrollable manufacturing randomness. Although PUFs provide the potential for lightweight and secure authentication for increasing numbers of low-end Internet of Things devices, practical and secure mechanisms remain elusive. We aim to explore simulatable PUFs (SimPUFs) that are physically unclonable but efficiently modeled mathematically through privileged one-time PUF access to address the above problem. Given a challenge, a securely stored SimPUF in possession of a trusted server computes the corresponding response and its bit-specific reliability. Consequently, naturally noisy PUF responses generated by a resource limited prover can be immediately processed by a one-way function (OWF) and transmitted to the server, because the resourceful server can exploit the SimPUF to perform a trial-and-error search over likely error patterns to recover the noisy response to authenticate the prover. Security of trial-and-error reverse (TREVERSE) authentication under the random oracle model is guaranteed by the hardness of inverting the OWF. We formally evaluate the TREVERSE authentication capability with two SimPUFs experimentally derived from popular silicon PUFs.

Trojan attacks on deep neural networks (DNNs) exploit abackdoorembedded in a DNN model that can hijack any input with an attacker’s chosen signature trigger. Emerging defence mechanisms are mainly designed and validated on vision domain tasks (e.g., image classification) on 2D Convolutional Neural Network (CNN) model architectures; a defence mechanism that is general across vision, text, and audio domain tasks is demanded. This work designs and evaluates a run-time Trojan detection method exploitingSTRongIntentionalPerturbation of inputs that is a multi-domain input-agnostic Trojan detection defence acrossVision,Text andAudio domains—thus termed as STRIP-ViTA. Specifically, STRIP-ViTA is demonstratively independent of not only task domain but also model architectures. Most importantly, unlike other detection mechanisms, it requires neither machine learning expertise nor expensive computational resource, which are the reason behind DNN model outsourcing scenario—one main attack surface of Trojan attack. We have extensively evaluated the performance of STRIP-ViTA over: i) CIFAR10 and GTSRB datasets using 2D CNNs for vision tasks; ii) IMDB and consumer complaint datasets using both LSTM and 1D CNNs for text tasks; and iii) speech command dataset using both 1D CNNs and 2D CNNs for audio tasks. Experimental results based on more than 30 tested Trojaned models (including publicly Trojaned model) corroborate that STRIP-ViTA performs well across all nine architectures and five datasets. Overall, STRIP-ViTA can effectively detect trigger inputs with small false acceptance rate (FAR) with an acceptable preset false rejection rate (FRR). In particular, for vision tasks, we can always achieve a 0 percent FRR and FAR given strong attack success rate always preferred by the attacker. By setting FRR to be 3 percent, average FAR of 1.1 and 3.55 percent are achieved for text and audio tasks, respectively. Moreover, we have evaluated STRIP-ViTA against a number of advanced backdoor attacks and compare its effectiveness with other recent state-of-the-arts.

This article presents two dynamic symmetric searchable encryption schemes for geometric range search. Our constructions are the first to provide forward/backward security in the context of SSE-based schemes supporting geometric range search. Besides, we define a security notion called content privacy. This security notion captures the leakages that are critical in the context of geometric range search but not considered by forward/backward security. Content privacy eliminates the leakage on the updated points of the database during both search and update. Due to the inherent leakages associated with range queries, none of the existing related works can support content privacy, whereas the design of our constructions avoids such leakages. When compared to the state-of-the-art schemes, our constructions provide a higher level of security and practical efficiency supported by our experimental results.

Medical time series data analytics based on dynamic time warping (DTW) greatly benefits modern medical research. Driven by the distributed nature of medical data, the collaboration of multiple healthcare institutions is usually necessary for a sound medical conclusion. Among others, a typical use case is disease screening for public health, where multiple healthcare institutions wish to collaboratively detect over their joint datasets the patients whose medical records have similar features to the given query samples. However, sharing the medical data faces critical privacy obstacles with the increasingly strict legal regulations on data privacy. In this article, we present the design of a novel system enabling privacy-preserving DTW-based analytics on distributed medical time series datasets. Our system is built from a delicate synergy of techniques from both cryptography and data mining domains, where the key idea is to leverage observations on the advancements in plaintext DTW analytics (e.g., clustering and pruning) to facilitate the scalable computation in the ciphertext domain, through our tailored security design. Extensive experiments over real medical time series datasets demonstrate the promising performance of our system, e.g., our system is able to process a secure DTW query computation over 15K time series sequences in 34 minutes.

Passwords are pervasively used to authenticate users’ identities in mobile apps. To secure passwords against attacks, protection is applied to the password authentication protocol (PAP). The implementation of the protection scheme becomes an important factor in protecting PAP against attacks. We focus on two basic protection in Android, i.e., SSL/TLS-based PAP and timestamp-based PAP. Previously, we proposed an automated tool,GLACIATE, to detect authentication flaws. We were curious whether orchestration (i.e., involving manual-effort) works better than automation. To answer this question, we propose an orchestrated approach,AuthExploitand compare its effectivenessGLACIATE. We study requirements for correct implementation of PAP and then applyGLACIATEto identify protection enhancements automatically. Through dependency analysis,GLACIATEmatches the implementations against the abstracted flaws to recognise defective apps. To evaluateAuthExploit, we collected 1,200 Android apps from Google Play. We comparedAuthExploitwith the automation tool,GLACIATE, and two other orchestration tools,${\sf MalloDroid}$and${\sf SMV-Hunter}$. The results demonstrated that orchestration tools detect flaws more precisely although the F1 score ofGLACIATEis higher thanAuthExploit. Further analysis of the results reveals that highly popular apps and e-commerce apps are not more secure than other apps.

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples. This becomes a critical problem since a defender usually does not have the type of attack asa prioriknowledge. Moreover, existing two-pronged defences (e.g., MagNet), which take advantage of both proactive and reactive methods, have been reported as broken under transferring attacks. To address this problem, this article proposed a novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks. The proposed defence first encodes training labels into label pairs and counters black-box attacks leveraging adversarial training supervised by the encoded label pairs. The defence further constructs a detector to identify and reject high-confidence adversarial examples that bypass the black-box defence. In addition, the proposed collaborative architecture can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed. In the experiments, we evaluated our defence against four state-of-the-art attacks on$MNIST$and$CIFAR10$datasets. The results showed that our defending method achieved up to 96.3 percent classification accuracy on black-box adversarial examples, and detected up to 98.7 percent of the high confidence adversarial examples. It only decreased the model accuracy on benign example classification by 2.1 percent for the$CIFAR10$dataset.

Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this article proposes a framework for securely and efficiently outsourcing decision tree inference. Targeting both privacy and efficiency, we propose a customized protocol using only lightweight cryptography in the online execution of secure inference. We resort to additive secret sharing and tackle the problems in various components including secure input feature selection, decision node evaluation, and inference result generation. Our protocol requires no interaction from the provider and client during online secure inference, a distinct advantage over prior works for practical deployment as they all operate under the client-provider setting where synchronous and continuous interaction is required. Performance evaluation demonstrates our security design's efficiency, as well as substantial performance benefits for the client (up to four orders of magnitude in computation and 163 times in communication), as opposed to prior art in the non-outsourcing setting. To facilitate the practical usage for meeting more service demands, we also investigate the extensions for secure outsourced inference of random forests and categorical feature-based decision trees.

Detection-based defense approaches are effective against adversarial attacks without compromising the structure of the protected model. However, they could be bypassed by stronger adversarial attacks and are limited in their ability to handle high-fidelity images. In this paper, we explore an effective detection-based defense against adversarial attacks on images (including high-resolution images) by extending the investigation beyond a single-instance perspective to incorporate its transformations as well. Our intuition is that the essential characteristics of a valid image are generally not affected by non-essential style transformations, for example, a slight variation in the facial expression of a portrait would not alter its identification. In contrast, adversarial examples are designed to affect only a single instance at a time, with unpredictable effects on a set of transformations of the instance. Consequently, we leverage a controllable generative mechanism to conduct the non-essential style transformations for a given image via modification along the style axis in the latent space. Next, the consistency of prediction between the given input and its style transformations is used to distinguish adversarial instances. Based on experiments on three image datasets, including high-resolution images, we demonstrated that our defense could detect 90–100 percent of adversarial examples produced by various state-of-the-art adversarial attacks, with a low false-positive rate.

Edge computing, as a relatively recent evolution of cloud computing architecture, is the newest way for enterprises to distribute computational power and lower repetitive referrals to central authorities. In the edge computing environment, Generative Models (GMs) have been found to be valuable and useful in machine learning tasks such as data augmentation and data pre-processing. Federated learning and distributed learning refer to training machine learning models in the edge computing network. However, federated learning and distributed learning also bring additional risks to GMs since all peers in the network have access to the model under training. In this article, we study the vulnerabilities of federated GMs to data-poisoning-based backdoor attacks via gradient uploading. We additionally enhance the attack to reduce the required poisonous data samples and cope with dynamic network environments. Last but not least, the attacks are formally proven to be stealthy and effective toward federated GMs. According to the experiments, neural backdoors can be successfully embedded by including merely 5\% poisonous samples in the local training dataset of an attacker.

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts from this grey literature can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by creating the patterns of the security knowledge gained from different sources. Prior studies neither systematically analysed the wide range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Moreover, existing topic modelling methods are not capable of identifying the cybersecurity concepts completely and the generated topics considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories to analyse trending topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings as follows: (1) The impact reflected from cybersecurity texts strongly correlates with the monetary loss caused by cybercrimes, (2) security blogs have produced the context of cybersecurity most intensively, and (3) websites deliver security information without caring about timeliness much.

The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms. Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally. However, it is also faced with expensive communication and limitations due to the heterogeneity of distributed data sources and lack of access to global data. In this paper, we investigate a practical distributed learning scenario where multiple downstream tasks (e.g., classifiers) could be efficiently learned from dynamically updated and non-iid distributed data sources while providing local data privatization. We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation. This scheme divides learning into (1) informative feature encoding, and transmitting the latent representation of local data to address communication overhead; (2) downstream tasks centralized at the server using the encoded codes gathered from each node to address computing overhead. Besides, a disentanglement strategy is applied to address the privatization of sensitive components of local data. Extensive experiments are conducted on image and speech datasets. The results demonstrate that downstream tasks with the compact latent representations with the privatization of local data can achieve comparable accuracy to centralized learning.

THE COVID-19 pandemic has brought the global society to a historic turning point. While the current pandemic is transforming our normal lives in an unprecedented way, the computing and information services ecosystem have provided the much-needed support to the continuity of the social interactions and the organizational functions. As COVID-19 continues to transform the global society, it is also exposing the weaknesses of the current services ecosystems and newer research challenges to further leveraging emerging computing and services technologies to provide secure, privacy-aware and resilient infrastructure and services during pandemics. The goal of this special issue has been to solicit impactful research papers that will be of immediate value to the ongoing COVID-19 pandemic as well as for future pandemic and crises.

Transfer learning provides an effective solution for feasibly and fast customize accurateStudentmodels, by transferring the learned knowledge of pre-trainedTeachermodels over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this article, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly adopted defenses:pruning-based,retraining-basedandinput pre-processing-based defenses. Specifically, ($\mathcal {A}$A) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeatingpruning-basedand/orretraining-based defenses. ($\mathcal {B}$B) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat theinput pre-processing-based defense, while guaranteeing that selected neuron(s) can be significantly activated. ($\mathcal {C}$C) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We launch effective misclassification attacks on Student models over real-world images, brain Magnetic Resonance Imaging (MRI) data and Electrocardiography (ECG) learning systems. The experiments reveal that our enhanced attack can maintain the 98.4 and 97.2 percent classification accuracy as the genuine model on clean image and time series inputs while improving$27.9\%-100\%$27.9%-100%and$27.1\%-56.1\%$27.1%-56.1%attack success rate on trojaned image and time series inputs respectively in the presence of pruning-based and/or retraining-based defenses.

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this article, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner. The intuition is that the classification result of a normal image is generally resistant to non-significant intrinsic feature changes, e.g., varying the thickness of handwritten digits. In contrast, adversarial examples are sensitive to such changes since the perturbation lacks transferability. To enable manipulation of features, a Combo-variational autoencoder is applied to learn disentangled latent codes that reveal semantic features. The resistance to classification change over the morphs, derived by varying and reconstructing latent codes, is used to detect suspicious inputs. Furthermore, Combo-VAE is enhanced to purify the adversarial examples with good quality by considering class-shared and class-unique features. We empirically demonstrate the effectiveness of detection and quality of purified instances. Our experiments on three datasets show that FM-Defense can detect nearly 100 percent of adversarial examples produced by different state-of-the-art adversarial attacks. It achieves more than 99 percent overall purification accuracy on the suspicious instances that close the manifold of clean examples.

As a promising technology in the development of human healthcare services, the wireless body area networks (WBANs) technology has attracted widespread attention in recent years from both industry and academia. However, due to the sensitiveness of the medical system and the capability limitation of the wearable devices, security, privacy, and efficiency of the healthcare services in WBANs are remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. In this article, we firstly provide a review and cryptanalysis on the state-of-the-art authentication scheme. In order to meet the challenges and address the drawbacks in previous works, we then propose a new efficient and anonymous authentication scheme for cloud based WBANs. Through the security analysis, we show that our scheme could overcome the weaknesses in previous schemes and meet all the security requirements. Besides, we show the advantages of the proposed scheme through performance evaluation in terms of functionality features, computation overhead, communication overhead and storage overhead, which shows our scheme is more appropriate for practical applications on healthcare services.

The amount of encrypted Internet traffic almost doubles every year thanks to the wide adoption of end-to-end traffic encryption solutions such as IPSec, TLS and SSH. Despite all the benefits of user privacy the end-to-end encryption provides, the encrypted internet traffic blinds intrusion detection system (IDS) and makes detecting malicious traffic hugely difficult. The resulting conflict between the user's privacy and security has demanded solutions for deep packet inspection (DPI) over encrypted traffic. The approach of those solutions proposed to date is still restricted in that they require intensive computations during connection setup or detection. For example, BlindBox, introduced by Sherry et al. (SIGCOMM 2015) enables inspection over the TLS-encrypted traffic without compromising users' privacy, but its usage is limited due to a significant delay on establishing an inspected channel. PrivDPI, proposed more recently by Ning et al. (ACM CCS 2019), improves the overall efficiency of BlindBox and makes the inspection scenario more viable. Despite the improvement, we show in this paper that the user privacy of Ning et al.'s PrivDPI can be compromised entirely by the rule generator without involving any other parties, including the middlebox. Having observed the difficulties of realizing efficiency and security in the previous work, we propose a new DPI system for encrypted traffic, named "Practical and Privacy-Preserving Deep Packet Inspection (P2DPI)''. P2DPI enjoys the same level of security and privacy that BlindBox provides. At the same time, P2DPI offers fast setup and encryption and outperforms PrivDPI. Our results are supported by formal security analysis. We implemented our P2DPI and comparable PrivDPI and performed extensive experimentation for performance analysis and comparison.

The proliferation of Internet of Things (IoT) devices has made people's lives more convenient, but it has also raised many security concerns. Due to the difficulty of obtaining and emulating IoT firmware, in the absence of internal execution information, black-box fuzzing of IoT devices has become a viable option. However, existing black-box fuzzers cannot form effective mutation optimization mechanisms to guide their testing processes, mainly due to the lack of feedback. In addition, because of the prevalent use of various and non-standard communication message formats in IoT devices, it is difficult or even impossible to apply existing grammar-based fuzzing strategies. Therefore, an efficient fuzzing approach with syntax inference is required in the IoT fuzzing domain.

Image-scaling is a typical operation that processes the input image before feeding it into convolutional neural network models. However, it is vulnerable to the newly revealed image-scaling attack. This work presents an image-scaling attack detection framework, Decamouflage, consisting of three independent detection methods: scaling, filtering, and steganalysis, to detect the attack through examining distinct image characteristics. Decamouflage has a pre-determined detection threshold that is generic. More precisely, as we have validated, the threshold determined from one dataset is also applicable to other different datasets. Extensive experiments show that Decamouflage achieves detection accuracy of 99.9% and 98.5% in the white-box and the black-box settings, respectively. We also measured its running time overhead on a PC with an Intel i5 CPU and 8GB RAM. The experimental results show that image-scaling attacks can be detected in milliseconds. Moreover, Decamouflage is highly robust against adaptive image-scaling attacks (e.g., attack image size variances).

A fundamental premise of SMS One-Time Password (OTP) is that the used pseudo-random numbers (PRNs) are uniquely unpredictable for each login session. Hence, the process of generating PRNs is the most critical step in the OTP authentication. An improper implementation of the pseudo-random number generator (PRNG) will result in predictable or even static OTP values, making them vulnerable to potential attacks. In this paper, we present a vulnerability study against PRNGs implemented for Android apps. A key challenge is that PRNGs are typically implemented on the server-side, and thus the source code is not accessible. To resolve this issue, we build an analysis tool, OTP-Lint, to assess implementations of the PRNGs in an automated manner without the source code requirement. Through reverse engineering, OTP-Lint identifies the apps using SMS OTP and triggers each app's login functionality to retrieve OTP values. It further assesses the randomness of the OTP values to identify vulnerable PRNGs. By analyzing 6,431 commercially used Android apps downloaded from Google Play and Tencent Myapp, OTP-Lint identified 399 vulnerable apps that generate predictable OTP values. Even worse, 194 vulnerable apps use the OTP authentication alone without any additional security mechanisms, leading to insecure authentication against guessing attacks and replay attacks.

Designing visually appealing layouts for multimedia documents containing text, graphs and images requires a form of creative intelligence. Modelling the generation of layouts has recently gained attention due to its importance in aesthetics and communication style. In contrast to standard prediction tasks, there are a range of acceptable layouts which depend on user preferences. For example, a poster designer may prefer logos on the top-left while another prefers logos on the bottom-right. Both are correct choices yet existing machine learning models treat layouts as a single choice prediction problem. In such situations, these models would simply average over all possible choices given the same input forming a degenerate sample. In the above example, this would form an unacceptable layout with a logo in the centre.

Fog computing extends the capability of cloud services to support latency sensitive applications. Adding fog computing nodes in proximity to a data generation/ actuation source can support data analysis tasks that have stringent deadline constraints. We introduce a real time, security-aware scheduling algorithm that can execute over a fog environment [1 , 2] . The applications we consider comprise of: (i) interactive applications which are less compute intensive, but require faster response time; (ii) computationally intensive batch applications which can tolerate some delay in execution. From a security perspective, applications are divided into three categories: public, private and semi-private which must be hosted over trusted, semi-trusted and untrusted resources. We propose the architecture and implementation of a distributed orchestrator for fog computing, able to combine task requirements (both performance and security) and resource properties.

Wireless body area networks (WBANs) technology nowadays has become a promising networking paradigm in the Internet of Things (IoT) as it can provide people with high quality of life and a high level of medical service. Specifically, to make sure those people with a high incidence of chronic diseases, including hypertension, diabetes, and cardiovascular diseases, are taking care of which further reduces social costs. Thus, both industry and academia in recent years pay widely attention to WBANs technology. After years of research, security, privacy, and efficiency of the healthcare services in WBANs have remained as major challenges. Although different authentication mechanisms have been designed to meet the challenges in recent years, most of them suffer from some functional defects or security problems. To ensure the security and privacy of patients’ sensitive biomedical data and the efficiency of message processing across different devices, it is critical to provide a secure and lightweight authentication scheme for WBANs.

Named Data Networking (NDN) is an emerging network architecture, which is built by keeping data as its pivotal point. The in-network cache, one of the important characteristics, makes data packets to be available from multiple locations on the Internet. Hence data access control and their enforcement mechanisms become even more critical in the NDNs. In this paper, we propose a novel encryption-based data access control scheme using Role-Based Encryption (RBE). The inheritance property of our scheme provides a natural way to achieve efficient data access control over hierarchical content. This in turn makes our scheme suitable for large scale real world content-centric applications and services such as Netflix. Further, the proposed scheme introduces an anonymous signature-based authentication mechanism to reject bogus data requests nearer to the source, thereby preventing them from entering the network. This in turn helps to mitigate better denial of service attacks. In addition, the signature mechanism supports unlinkability, which is essential to prevent leakages of individual user's access patterns. Another major feature of the proposed scheme is that it provides accountability of the Internet Service Providers (ISPs) using batch signature verification. Moreover, we have developed a transparent and secure dispute resolution and payment mechanism using smart-contract and blockchain technologies. We present a formal security analysis of our scheme to show it is provably secure against Chosen Plaintext Attacks. We also demonstrate that our scheme supports more functionalities than the existing schemes and its performance is better in terms of computation, communication and storage.

Abstract Biometric information is unique to a human, so it would be desirable to use the biometric characteristic as the private key in a cryptographic system to protect data security and privacy. In this paper, we introduce a notion called two-factor decryption (TFD). Informally speaking, a TFD scheme is a variant of the public-key encryption (PKE) scheme. In a TFD scheme, messages are encrypted under public keys as that in a standard PKE scheme, but both private keys (i.e. the first factor) and biometric inputs (i.e. the second factor) are required to decrypt the ciphertexts and obtain the underlying plaintexts. We first describe a framework of TFD, and then define a formal security model for TFD. Thereafter, we present a generic construction on TFD based on the cryptographic primitives of linear sketch and functional encryption (FE) with certain properties and analyse its security. In addition, we give instantiations of TFD by applying concrete FE schemes into the generic construction and show their applications.

The Internet of Things (IoT) is increasingly being used in applications ranging from precision agriculture to critical national infrastructure by deploying a large number of resource-constrained devices in hostile environments. These devices are being exploited to launch attacks in cyber systems. As a result, security has become a significant concern in the design of IoT-based applications. In this article, we present a security architecture for IoT networks by leveraging the underlying features supported by software-defined networks (SDNs). Our security architecture not only restricts network access to authenticated IoT devices but also enforces fine granular policies to secure the flows in the IoT network infrastructure. The authentication is achieved using a lightweight protocol to authenticate IoT devices. Authorization is achieved using a dynamic policy driven approach. Such an integrated security approach involving authentication of IoT devices and enables authorized flows to protect IoT networks from malicious IoT devices and attacks. We have implemented and validated our architecture using ONOS SDN Controller and Raspbian Virtual Machines, and demonstrated how the proposed security mechanisms can counteract malware packet injection, DDoS attacks using Mirai, spoofing/masquerading, and man-in-the-middle attacks. An analysis of the security and performance of the proposed security mechanisms and their applications is presented in this article.

In a typical e-healthcare system, it is common for users' physiological data collected by Internet-of-Things (IoT) devices to be processed and shared in a third-party environment. To improve service quality, healthcare data sharing in third-party environments needs to ensure the integrity, source authentication, and privacy of the data. Redactable signature schemes (RSSs) are designed to address this concern over the past decades. More concretely, an RSS allows a signature holder to delete privacy-sensitive parts of the signed data and derive a valid signature for the retained data without any help from the original signer. This also provides a flexible data sharing mechanism in a bandwidth-saving manner. However, almost all of the existing RSSs are built on top of public-key infrastructure (PKI) systems, which involve heavyweight public-key management problems and are not suitable for resource-limited IoT applications. Besides, we argue that the only known PKI independent RSS for IoT has some security flaws and requires a large storage space. In this work, we eliminate some of the costs associated with PKI and certificates (such as key managements and certificate verifications) in traditional RSS and propose the first identity-based RSS satisfying the requirements of protecting the integrity and source authentication with selective disclosure control for healthcare data sharing in IoT. We prove the security of the scheme in the random oracle model under the k-SDH assumption. Theoretical comparison and experimental analysis show that our construction has a practical performance. As an extension, we also discuss how to extend our design to achieve fine-grained redaction control, which provides a feasible strategy for a signer to prevent additional redaction or arbitrary redaction from dishonest signature holders.

With the ever-increasing maturity and popularization of the Internet of Things (IoT), tremendous business applications developed by distinct enterprises or organizations have been encapsulated into lightweight web services that can easily be accessed or invoked remotely. However, the big volume of candidate web services places a heavy burden on the users' service selection decision-making process. Under the circumstance, a variety of intelligent recommendation solutions have been developed to reduce the high decision-making cost. Traditional resolutions usually challenge in two aspects. First, the recommendation parameters, i.e., the quality of services (QoS), usually relies on user/service location heavily; therefore, low-quality recommended results may be returned to users if user/service location information is overlooked. Second, historical QoS data often contain partial sensitive information of users; therefore, it becomes a necessity to protect the sensitive QoS data while making accurate recommendation decisions. To tackle the above challenges, we introduce the concepts of user/service location information and locality-sensitive hashing (LSH) in the domain and propose a location-aware recommendation approach with privacy-preservation capability. A wide range of experiments is set up based on the popular WS-DREAM data set, whose results prove the effectiveness and efficiency of our approach.

All the state-of-the-art rowhammer attacks can break the MMU-enforced inter-domain isolation because the physical memory owned by each domain is adjacent to each other. To mitigate these attacks, physical domain isolation, introduced by CATT, physically separates each domain by dividing the physical memory into multiple partitions and keeping each partition occupied by only one domain. CATT implemented physical kernel isolation as the first generic and practical software-only defense to protect kernel from being rowhammered as kernel is one of the most appealing targets. In this paper, we develop a novel exploit that could effectively defeat the CATT implementation and gain both root and kernel privileges, indicating that the physical kernel isolation is not secure in practice. Our exploit can work without exhausting the page cache or the system memory, or relying on the information of the virtual-to-physical address mapping. The exploit is motivated by our key observation that the modern OSes have double-owned kernel buffers (e.g., video buffers and SCSI Generic buffers) owned concurrently by the kernel and user domains. The existence of such buffers invalidates the physical separation enforced by CATT and makes the rowhammer-based attack possible again. Existing conspicuous rowhammer attacks achieving the root/kernel privilege escalation exhaust the page cache or even the whole system memory. Instead, we propose a new technique, named Memory Ambush. It is able to place the hammerable double-owned kernel buffers physically adjacent to the target objects (e.g., page tables) with only a small amount of memory. As a result, our exploit is stealthier and has fewer memory footprints. We also replace the inefficient rowhammer algorithm that blindly picks up addresses to hammer with an efficient one. Our algorithm selects suitable addresses based on an existing timing channel. We implement our exploit on the Linux kernel version 4.10.0. Our experiment results indicate that a successful attack could be done within 1 minute. The occupied memory is as low as 88 MB.

The papers in this special section focus on secure and emerging collaborative computing and intelligent systems. The Internet, coupled with recent advances in computing and information technologies, such as IoT, mobile edge/ cloud computing, cyber-physical-social systems, and artificial intelligence/machine learning/deep learning, have paved the way for creating next-generation smart and intelligent systems and applications that can have transformative impact in our society while accelerating rapid scientific discoveries and innovations. Unprecedented cyber-social and cyber-physical infrastructures and systems that span geographic boundaries are possible because of the Internet and the growing number of collaboration-enabling technologies. With newer technologies and paradigms getting increasingly embedded in the computing platforms and networked information systems/ infrastructures that form the digital foundation for our personal, organizational, and social processes and activities, it is increasingly becoming critical that the trust, privacy, and security issues in such digital environments are holistically addressed to ensure the safety and well-being of individuals as well as our society.

We propose a multi-client Symmetric Searchable Encryption (SSE) scheme based on the single-user protocol [3] . The scheme allows any user to generate a search query by interacting with any θ is a threshold parameter) `helping' users. It preserves the privacy of a database content against the server assuming a leakage of up to θ-1 users' keys to the server while hiding the query from the θ-1 `helping users'. To achieve the query privacy, we design a new distributed key-homomorphic pseudorandom function (PRF) that hides the PRF input (search keyword) from the `helping' users. We present a concrete construction of our randomizable distributed PRF. By distributing the utilized keys among the users, the need for a constant online presence of the data owner to provide services to the users is eliminated, while providing resilience against a user key exposure. We extended our scheme to support user revocation in two different scenarios. In addition, we give a solution for fast update of the encryption key with the overhead significantly smaller than the re-encryption and re-uploading the database. Moreover, our scheme is secure against passive and active collusion between the server and a subset of users.

Deep Neural Networks (DNNs) are vulnerable to deliberately crafted adversarial examples. In the past few years, many efforts have been spent on exploring query-optimisation attacks to find adversarial examples of either black-box or white-box DNN models, as well as the defending countermeasures against those attacks. In this article, we explore vulnerabilities of DNN models under the umbrella of Man-in-the-Middle (MitM) attacks, which have not been investigated before. From the perspective of an MitM adversary, the aforementioned adversarial example attacks are not viable anymore. First, such attacks must acquire the outputs from the models multiple times before actually launching attacks, which is difficult for the MitM adversary in practice. Second, such attacks are one-off and cannot be directly generalised onto new data examples, which decreases the rate of return for the attacker. In contrast, using generative models to craft adversarial examples on the fly can mitigate the drawbacks. However, the adversarial capability of the generative models, such as Variational Auto-Encoder (VAE), has not been extensively studied. Therefore, given a classifier, we investigate using a VAE decoder to either transform benign inputs to their adversarial counterparts or decode outputs from benign VAE encoders to be adversarial examples. The proposed method can endue more capability to MitM attackers. Based on our evaluation, the proposed attack can achieve above 95 percent success rates on both MNIST and CIFAR10 datasets, which is better or comparable with state-of-the-art query-optimisation attacks. In the meantime, the attack is 104times faster than the query-optimisation attacks.

Compression is widely used in Internet applications to save communication time, bandwidth and storage. Recently invented by Jarek Duda asymmetric numeral system (ANS) offers an improved efficiency and a close to optimal compression. The ANS algorithm has been deployed by major IT companies such as Facebook, Google and Apple. Compression by itself does not provide any security (such as confidentiality or authentication of transmitted data). An obvious solution to this problem is an encryption of compressed bitstream. However, it requires two algorithms: one for compression and the other for encryption. In this work, we investigate natural properties of ANS that allow to incorporate authenticated encryption using as little cryptography as possible. We target low-level security communication and storage such as transmission of data from IoT devices/sensors. In particular, we propose three solutions for joint compression and encryption (compcrypt). The solutions offer different tradeoffs between security and efficiency assuming a slight compression deterioration. All of them use a pseudorandom bit generator (PRBG) based on lightweight stream ciphers. The first solution is close to original ANS and applies state jumps controlled by PRBG. The second one employs two copies of ANS, where compression is switched between the copies. The switch is controlled by a PRBG bit. The third compcrypt modifies the encoding function of ANS depending on PRBG bits. Security and efficiency of the proposed compcrypt algorithms are evaluated. The first compcrypt is the most efficient with a slight loss of compression quality. The second one consumes more storage but the loss of compression quality is negligible. The last compcrypt offers the best security but is the least efficient.

Recent years have witnessed the proliferation of the deployment of virtualization techniques. Virtualization is designed to be transparent, that is, unprivileged users should not be able to detect whether a system is virtualized. Such detection can result in serious security threats such as evading virtual machine (VM)-based malware dynamic analysis and exploiting vulnerabilities for cross-VM attacks. The traditional software-based virtualization leaves numerous artifacts/fingerprints, which can be exploited without much effort to detect the virtualization. In contrast, current mainstream hardware-assisted virtualization significantly enhances the virtualization transparency, making itself more transparent and difficult to be detected. Nonetheless, we showcase three new identified low-level inconspicuous features, which can be leveraged by an unprivileged adversary to effectively and stealthily detect the hardware-assisted virtualization. All three features come from the chipset fingerprints, rather than the traces of software-based virtualization implementations (e.g., Xen or KVM). The identified features include i) Translation-Lookaside Buffer (TLB) stores an extra layer of address translations; ii) Last-Level Cache (LLC) caches one more layer of page-table entries; and iii) Level-1 Data (L1D) Cache is unstable. Based on the above features, we develop three corresponding virtualization detection techniques, which are then comprehensively evaluated on three native environments and three popular cloud providers: i) Amazon Elastic Compute Cloud, ii) Google Compute Engine and iii) Microsoft Azure. Experimental results validate that these three adversarial detection techniques are effective (with no false positive) and stealthy (without triggering suspicious system events, e.g., VM-exit) in detecting the above commodity virtualized environments.

Rowhammer is a destructive software-induced DRAM fault, which an attacker can leverage to break system security. Both individual customers and enterprise users (e.g., cloud providers) might refrain from using a computing system if it is vulnerable to rowhammer vulnerability. In this paper, we provide the first end-to-end tool, coined BitMine, that systematically assesses a DRAM chip’s vulnerability to rowhammer bit flips. BitMine is an extension of DRAMDig. As DRAM address mappings are proprietary techniques and critical in inducing rowhammer bit flips, DRAMDig, our prior work, leverages domain knowledge to efficiently and deterministically reverse-engineer DRAM address mappings on Intel machines. By incorporating DRAMDig, BitMine configures three key parameters, i.e., hammer methods, hammer patterns, data patterns, on the effectiveness of finding rowhammer bit flips. BitMine by default implements 13 hammer methods, 4 hammer patterns and 16 data patterns and is extensible to support more. We evaluate DRAMDig and BitMine against multiple machine models that combine different DRAM chips and Intel microarchitectures. Our experiment results show that DRAMDig efficiently uncovers a deterministic DRAM address mapping for each machine model, and every implemented parameter in BitMine has its distinct effectiveness in triggering bit flips for different machine models.

Driverless parking, an influential application of Mobility as a Service (MaaS) model, is one of the clear early benefits for autonomous vehicles, given often narrow spaces and multiple potential hazards (such as pedestrians stepping out from in between other vehicles). In recent years, real momentum has been building up for designing automated parking models for vehicles. However, in such an autonomous parking design, location privacy and identity privacy issues are always overlapping due to the improper sharing of data. Most existing studies barely investigate and poorly address such privacy issues. Motivated by this, we develop (and evaluate) an experience-driven, secure and privacy-aware framework of parking reservations for automated cars. Our idea of using differential privacy with zero-knowledge proof provides both security and privacy guarantees to users. Furthermore, the performance of the developed model is enhanced by exploiting reinforcement learning approach such that the utility of the system and the parking reservation rate can be maximized. Extensive evaluation demonstrates the superiority of the proposed model.

The personalized Web service recommendation based on Quality of Service (QoS) is gaining increasing popularity due to its promising ability to help users find high quality services. Studies suggest that it is beneficial to use Collaborative Filtering (CF)-based techniques to facilitate Web service recommendations which can achieve high accuracy in predicting the QoS for unobserved Web services. With the QoS, location of users and Web services has been another significant factor in predicting the QoS values. The more factors that are available to the service providers, the more accurate predictions can be generated. However these factors are privacy sensitive and therefore it is risky to disclose them to any third party service provider. To address this challenge, in this paper we develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. Our protocol is able to achieve user privacy by means of encrypting the QoS and location as well as to select suitable Web services for users without disclosing any private information. We conduct extensive experimental analysis on publicly available data sets and prove that our method is both secure and practical.

Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as anunbounded knapsack problem, and resource scaling as anone-step ahead resource allocation problem. Based on these models, we propose two scaling strategies: (a)delta capacity optimization, which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b)full capacity optimization, which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach.

Fog computing brings the cloud closer to a user with the help of a micro data center ($mdc$), leading to lower response times for delay sensitive applications.RT-SANE(Real-TimeSecurityAware scheduling on theNetworkEdge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an$mdc$(in proximity to a user) and a cloud data center ($cdc$) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and$mdcs$and$cdcs$are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user’s local$mdcs$or pre-trusted$cdcs$, and semi-private and public jobs on remote$mdcs$and$cdcs$. A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal thatRT-SANEoffers a higher “success ratio” (successfully completed jobs) to comparable algorithms, including consideration of security tags.

Wi-Fi hotspot-based data clone services are increasingly used by Android users to transfer their user data and preferred configurations while upgrading obsolete phones to new models. Unfortunately, since the data clone services need to manipulate sensitive information protected by the Android system, vulnerabilities in the design or implementation of these services may result in data privacy breaches. In this paper we present an empirical security analysis of eight widely used Wi-Fi hotspot-based data clone services deployed to millions of Android phones. Our study evaluates those services with respect to data export/import, data transmission, and Wi-Fi configuration with respect to security requirements that the data clone procedure should satisfy. Since data clone services are closed source, we design Poirot, an analysis system to recover workflows of the data clone services and detect potential flaws. Our study reveals a series of critical security issues in the data clone services. We demonstrate two types of attacks that exploit the data clone service as a new attack surface. A vulnerable data clone service allows attackers to retrieve sensitive user data without permissions, and even inject malicious contents to compromise the system.

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server. Therefore, the server has no direct access to raw data processed at the client. Until now, the split learning is believed to be a promising approach to protect the client's raw data; for example, the client's data was protected in healthcare image applications using 2D convolutional neural network (CNN) models. However, it is still unclear whether the split learning can be applied to other deep learning models, in particular, 1D CNN.

Users receive a multitude of security information in written articles, e.g., newspapers, security blogs, and training materials. However, prior research suggests that these delivery methods, including security awareness campaigns, mostly fail to increase people's knowledge about cyber threats. It seems that users find such information challenging to absorb and understand. Yet, to raise users' security awareness and understanding, it is essential to ensure the users comprehend the provided information so that they can apply the advice it contains in practice. We conducted a subjective study to measure the level of users' understanding of security texts. We find that 61% of the terms security experts used in their writings are hard for the public to understand, even for people with some IT backgrounds. We also observe that 88% of security texts have at least one such term. Moreover, we notice that existing dictionaries, including the online ones (e.g., Google Dictionary), cover no more than 35% of the terms found in security texts. To improve users' ability to understand security texts, we developed a framework to build a user-oriented security-centric dictionary from multiple sources. To evaluate the effectiveness of the dictionary, we developed a tool as a service to detect technical terms and explain their meanings to the user in pop-ups. The results of a subjective study to measure the tool's performance showed that it could increase users' ability to understand security articles by 30%.

As recently emerged rowhammer exploits require undocumented DRAM address mapping, we propose a generic knowledge-assisted tool, DRAMDig, which takes domain knowledge into consideration to efficiently and deterministically uncover the DRAM address mappings on any Intel-based machines. We test DRAMDig on a number of machines with different combinations of DRAM chips and microarchitectures ranging from Intel Sandy Bridge to Coffee Lake. Comparing to previous works, DRAMDig deterministically reverse-engineered DRAM address mappings on all the test machines with only 7.8 minutes on average. Based on the uncovered mappings, we perform double-sided rowhammer tests and the results show that DRAMDig induced significantly more bit flips than previous works, justifying the correctness of the uncovered DRAM address mappings.

We address the issue of having a limited number of annotations for stance classification in a new domain, by adapting out-of-domain classifiers with domain adaptation. Existing approaches often align different domains in a single, global feature space (or view), which may fail to fully capture the richness of the languages used for expressing stances, leading to reduced adaptability on stance data. In this paper, we identify two major types of stance expressions that are linguistically distinct, and we propose a tailored dual-view adaptation network (DAN) to adapt these expressions across domains. The proposed model first learns a separate view for domain transfer in each expression channel and then selects the best adapted parts of both views for optimal transfer. We find that the learned view features can be more easily aligned and more stance-discriminative in either or both views, leading to more transferable overall features after combining the views. Results from extensive experiments show that our method can enhance the state-of-the-art single-view methods in matching stance data across different domains, and that it consistently improves those methods on various adaptation tasks.

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user’s update. Inspired by the two challenges, we propose FEDXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FEDXGB mainly achieves the following two breakthroughs. First, FEDXGB involves a new HE based secure aggregation scheme for FL. By combining the advantages of secret sharing and homomorphic encryption, the algorithm can solve the second challenge mentioned above, and is robust to the user dropout. Then, FEDXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. Moreover, we conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FEDXGB. The results indicate that FEDXGB achieves less than 1% accuracy loss compared with the original XGBoost, and can provide about 23.9% runtime and 33.3% communication reduction for HE based model update aggregation of FL.

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

Anomaly detection aims to recognize samples with anomalous and unusual patterns with respect to a set of normal data. This is significant for numerous domain applications, such as industrial inspection, medical imaging, and security enforcement. There are two key research challenges associated with existing anomaly detection approaches: (1) many approaches perform well on low-dimensional problems however the performance on high-dimensional instances, such as images, is limited; (2) many approaches often rely on traditional supervised approaches and manual engineering of features, while the topic has not been fully explored yet using modern deep learning approaches, even when the well-label samples are limited. In this paper, we propose a One-for-all Image Anomaly Detection system (OIAD) based on disentangled learning using only clean samples. Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency. We implement this idea and evaluate its performance for anomaly detection. Our experiments with three datasets show that OIAD can detect over 90% of anomalies while maintaining a low false alarm rate. It can also detect suspicious samples from samples labeled as clean, coincided with what humans would deem unusual.

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhammer bypasses all of the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures.Recently, several proposals suggest exploiting the spatial proximity between the accessed memory location and the location of the bit flip for a defense against rowhammer. These all aim to deny the attacker's permission to access memory locations near sensitive data.In this paper, we question the core assumption underlying these defenses. We present PThammer, a confused-deputy attack that causes accesses to memory locations that the attacker is not allowed to access. Specifically, PThammer exploits the address translation process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. We implement PThammer, demonstrating that it is a viable attack, resulting in a system compromise (e.g., kernel privilege escalation). We further evaluate the effectiveness of proposed software-only defenses showing that PThammer can overcome those.

Internet of Medical Things (IoMT) are getting popular in the smart healthcare domain. These devices are resource-constrained and are vulnerable to attack. As the IoMTs are connected to the healthcare network infrastructure, it becomes the primary target of the adversary due to weak security and privacy measures. In this regard, this paper proposes a security architecture for smart healthcare network infrastructures. The architecture uses various security components or services that are developed and deployed as virtual network functions. This makes the security architecture ready for future network frameworks such as OpenMANO. Besides, in this security architecture, only authenticated and trusted IoMTs serve the patients along with an encryption-based communication protocol, thus creating a secure, privacy-preserving and trusted healthcare network infrastructure.

Federated learning (FL) and split neural networks (SplitNN) are state-of-art distributed machine learning techniques to enable machine learning without directly accessing raw data on clients or end devices. In theory, such distributed machine learning techniques have great potential in distributed applications, in which data are typically generated and collected at the client-side while the collected data should be processed by the application deployed at the server-side. However, there is still a significant gap in evaluating the performance of those techniques concerning their practicality in the Internet of Things (IoT)-enabled distributed systems constituted by resource-constrained devices. This work is the first attempt to provide empirical comparisons of FL and SplitNN in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, different model architectures, multiple clients, and various performance metrics. For the learning performance (i.e., model accuracy and convergence time), we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data. We show that the learning performance of SplitNN is better than FL under an imbalanced data distribution but worse than FL under an extreme non-IID data distribution. For implementation overhead, we mount both FL and SplitNN on Raspberry Pi devices and comprehensively evaluate their overhead, including training time, communication overhead, power consumption, and memory usage. Our key observations are that under the IoT scenario where the communication traffic is the primary concern, FL appears to perform better over SplitNN because FL has a significantly lower communication overhead compared with SplitNN. However, our experimental results also demonstrate that neither FL or SplitNN can be applied to a heavy model, e.g., with several million parameters, on resource-constrained IoT devices because its training cost would be too expensive for such devices. Source code is released and available: https://github.com/Minki-Kim95/Federated-Learning-and-Split-Learning-with-raspberry-pi.

The immutable feature of blockchain determines that traditional security response mechanisms (e.g., code patching) must change to remedy insecure smart contracts. The only proper way to protect a smart contract is to fix potential risks in its code before it is deployed to the blockchain. However, existing tools for smart contract security analysis focus on the detection of bugs but seldom consider the code fix issues. Meanwhile, it is often time-consuming and error-prone for a developer to understand and fix flawed code manually. In this paper we propose SMARTSHIELD, a bytecode rectification system, to fix three typical security-related bugs (i.e., state changes after external calls, missing checks for out-of-bound arithmetic operations, and missing checks for failing external calls) in smart contracts automatically and help developers release secure contracts. Moreover, SMARTSHIELD guarantees that the rectified contract is not only immune to certain attacks but also gas-friendly (i.e., a slightly increase of gas cost). To evaluate the effectiveness and efficiency of SMARTSHIELD, we applied it to 28,621 real-world buggy contracts on Ethereum blockchain (as of January 2nd2019). Experiment results demonstrated that among 95,502 insecure cases in those contracts, 87,346 (91.5%) of them were automatically fixed by SMARTSHIELD. A following test with both program analysis and real-world exploits further testified that the rectified contracts were secure against common attacks. Moreover, the rectification only introduced a 0.2 % gas increment for each contract on average.

Machine learning-based solutions have been successfully employed for the automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features (e.g., requested permissions, API calls,etc.), and the perturbations can only be implemented by simply modifying application’s manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning-based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK’s Dalvik bytecode rather than Android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art Android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.

The concept of functional encryption (FE) has been introduced to address the shortcomings of public-key encryption (PKE) in many emerging applications which require both data storage and data sharing (e.g., cloud storage service). One of the major issues existing in most FE schemes is the efficiency, as they are built from bilinear pairings of which the computation is very expensive. A widely accepted solution to this problem is outsourcing the heavy workloads to a powerful third party and leaving the user with the light computation. Nevertheless, it is impractical to assume that the third party (e.g., the cloud) will provide free services. To our knowledge, no attention has been paid to the payment procedure between the user and the third party in an FE with outsourced decryption (FEOD) scheme under the assumption that neither of them should be trusted. Leveraging the transactions on cryptocurrencies supported by the blockchain technology, in this paper, we aim to design FE with payable outsourced decryption (FEPOD) schemes. The payment in an FEPOD scheme is achieved through a blockchain-based cryptocurrency, which enables the user to pay a third party when it correctly completes the outsourced decryption. We define the adversarial model for FEPOD schemes, and then present a generic construction of FEPOD schemes. Also, we evaluate the performance of the proposed generic construction by implementing a concrete FEPOD scheme over a blockchain platform.

In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to identify users whose profiles match the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online dating website, Ashley Madison, was hacked, which resulted in a disclosure of a large number of dating user profiles. This data breach has urged researchers to explore practical privacy protection for user profiles in a social network. In this paper, we propose a privacy-preserving solution for profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out matching users with the help of multiple servers without revealing to anyone the query and the queried user profiles in clear. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our experiments demonstrate that our solution is practical.

The overwhelming amount of information continuously flowing through the Twitter environment makes topic derivation essential. It indeed plays a valuable role in a variety of Twitter-based applications, including content recommendations, news summarization, market analysis, etc. Topic derivation methods are typically based on semantic features of tweet contents. Because tweets are short by nature, such methods suffer from data sparsity. To alleviate this problem, this paper proposes a topic derivation method that incorporates tweet text similarity and interactions measures. Besides the tweet contents, the approach takes into account several types of interactions amongst tweets: Tweets which mention the same people, replies and retweets. Topic derivation is done through a two-step matrix factorization process. We conducted a number of experiments on several Twitter datasets to reveal both the individual and integrated effects of the various features being considered. Our experimental results against TREC2014 and our self collected tweetMarch datasets demonstrate that the proposed method is able to provide more than 30 percent improvement compared to other advanced topic derivation methods.

We identify agreement and disagreement between utterances that express stances towards a topic of discussion.Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference.We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared.To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements.We propose a reason comparing network (RCN) to leverage reason information for stance comparison.Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection.

A recent trojan attack on deep neural network (DNN) models is one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker's chosen trojan trigger. Since the trojan trigger is a secret guarded and exploited by the attacker, detecting such trojan inputs is a challenge, especially at run-time when models are in active operation. This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of predicted classes for perturbed inputs from a given deployed model---malicious or benign. A low entropy in predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input---a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on three popular and contrasting datasets: MNIST, CIFAR10 and GTSRB. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers. Using CIFAR10 and GTSRB, we have empirically achieved result of 0% for both FRR and FAR. We have also evaluated STRIP robustness against a number of trojan attack variants and adaptive attacks.

A great quantity of user passwords nowadays has been leaked through security breaches of user accounts. To enhance the security of the Password Authentication Protocol (PAP) in such circumstance, Android app developers often implement a complementary One-Time Password (OTP) authentication by utilizing the short message service (SMS). Unfortunately, SMS is not specially designed as a secure service and thus an SMS One-Time Password is vulnerable to many attacks. To check whether a wide variety of currently used SMS OTP authentication protocols in Android apps are properly implemented, this paper presents an empirical study against them. We first derive a set of rules from RFC documents as the guide to implement secure SMS OTP authentication protocol. Then we implement an automated analysis system, AUTH-EYE, to check whether a real-world OTP authentication scheme violates any of these rules. Without accessing server source code, AUTH-EYE executes Android apps to trigger the OTP-relevant functionalities and then analyzes the OTP implementations including those proprietary ones. By only analyzing SMS responses, AUTH-EYE is able to assess the conformance of those implementations to our recommended rules and identify the potentially insecure apps. In our empirical study, AUTH-EYE analyzed 3,303 popular Android apps and found that 544 of them adopt SMS OTP authentication. The further analysis of AUTH-EYE demonstrated a far-from-optimistic status: the implementations of 536 (98.5%) out of the 544 apps violate at least one of our defined rules. The results indicate that Android app developers should seriously consider our discussed security rules and violations so as to implement SMS OTP properly.

Each layer of nodes and communication networks in edge computing, from cloud to the end device (i.e, often considered as resource-constrained IoT devices), exhibits a different level of trust for each stakeholder - e.g., edge nodes may not be fully trusted by IoT devices and the cloud. Moreover, asymmetric nature of resources between layers makes it hard to establish a balance between security and performance - e.g., lightweight cryptography may degrade security level against untrusted nodes while heavyweight ones may not be feasible for the light-weight end devices. An advanced encryption scheme such as the Identity-Based Broadcast Encryption (IBBE) is a popular technique to reduce storage and communication overhead. However, IBBE requires heavy computation to the end devices and still does not fully satisfy the security requirements that exist in the layers of edge computing. This paper presents a new IBBE with outsourced partial decryption for hybrid security models that each layer in edge computing requires. It balances the computational overhead based on asymmetric nature that nodes in each layer have. Particularly, with new schemes, the ciphertext can be transformed from its initial format. The cloud encrypts their data for multiple end devices and store them in the edge nodes, but those interim nodes can blindly transform the ciphertext from the cloud into a form which (i) is decryptable by only an authorized end device, and (ii) imposes smaller decryption and data transmission burden to end devices, regardless of the number of recipients. Our security analysis shows that new schemes are selectively and adaptively secure. We implement our solution and show that new schemes reduce the communication overhead from an edge node to end devices and the computation overhead on the end devices, compared to the original IBBE schemes.

This talk aims to highlight the current culture of blaming the victims prevalent in the cybersecurity research community, present the current research initiatives in human-centric cybersecurity, and outline the potential future research areas.

The Internet of Things (IoT) provides connectivity between heterogeneous devices in different applications, such as smart wildlife, supply chain and traffic management. Trust management system (TMS) assesses the trustworthiness of service with respect to its quality. Under different context information, a service provider may be trusted in one context but not in another. The existing context-aware trust models usually store trust values under different contexts and search the closest (to a given context) record to evaluate the trustworthiness of a service. However, it is not suitable for distributed resource-constrained IoT devices which have small memory and low power. Reputation systems are applied in many trust models where trustor obtains recommendations from others. In context-based trust evaluation, it requires interactive queries to find relevant information from remote devices. The communication overhead and energy consumption are issues in low power networks like 6LoWPAN. In this paper, we propose a new context-aware trust model for lightweight IoT devices. The proposed model provides a trustworthiness overview of a service provider without storing past behavior records, that is, constant size storage. The proposed model allows a trustor to decide the significance of context items. This could result in distinctive decisions under the same trustworthiness record. We also show the performance of the proposed model under different attacks.

With the prevalence of location-aware devices and applications, enormous volumes of human spatiotemporal trajectories are being produced. It is feasible to estimate the similarity between user movement patterns according to such trajectories, which can be regarded as a potential social tie between users. There are two key research challenges associated with social tie discovery from trajectories: (1) trajectories contain users' accurate locations and releasing such data for social tie discovery raises serious privacy concerns; (2) trajectories are archived as discrete approximations of actual movement patterns using different sampling strategies and rates which are intrinsically heterogeneous. To address these challenges, this paper proposes a Privacy-protected Social Tie Mining (P-STM) approach. It provides a new social tie discovery solution based on the similarity of calibrated trajectories incorporating three key components: (1) a location entropy-based indicative dense region (IDR) mining approach to handle the heterogeneity of trajectories under differential privacy; (2) a private model-based calibration system used to rewrite trajectories using a sanitized IDR set to improve the utility of sanitized trajectories for similarity evaluation; (3) a social tie mining approach to indicate potential social ties between individuals using the similarity trajectories, which aims at finding the acquaintances for users based on solely their local geographical activities. The proposed approach is evaluated using real-world trajectory datasets from location-based social networks.

A wide variety of security software systems need to be integrated into a Security Orchestration Platform (SecOrP) to streamline the processes of defending against and responding to cybersecurity attacks. Lack of interpretability and interoperability among security systems are considered the key challenges to fully leverage the potential of the collective capabilities of different security systems. The processes of integrating security systems are repetitive, time-consuming and error-prone; these processes are carried out manually by human experts or using ad-hoc methods. To help automate security systems integration processes, we propose an Ontology-driven approach for Security OrchestrAtion Platform (OnSOAP). The developed solution enables interpretability, and interoperability among security systems, which may exist in operational silos. We demonstrate OnSOAP's support for automated integration of security systems to execute the incident response process with three security systems (Splunk, Limacharlie, and Snort) for a Distributed Denial of Service (DDoS) attack. The evaluation results show that OnSOAP enables SecOrP to interpret the input and output of different security systems, produce error-free integration details, and make security systems interoperable with each other to automate and accelerate an incident response process.

Summary 6LoWPAN networks involving wireless sensors consist of resource starving miniature sensor nodes. Since secured authentication is one of the important considerations, the use of asymmetric key distribution scheme may not be a perfect choice. Recent research shows that Lucky Thirteen attack has compromised Datagram Transport Layer Security (DTLS) with Cipher Block Chaining (CBC) mode for key establishment. Even though EAKES6Lo and S3 K techniques for key establishment follow the symmetric key establishment method, they strongly rely on a remote server and trust anchor. Our proposed Lightweight Authentication Protocol (LAUP) used a symmetric key method with no preshared keys and comprised of four flights to establish authentication and session key distribution between sensors and Edge Router in a 6LoWPAN environment. Each flight uses freshly derived keys from existing information such as PAN ID (Personal Area Network IDentification) and device identities. We formally verified our scheme using the Scyther security protocol verification tool. We simulated and evaluated the proposed LAUP protocol using COOJA simulator and achieved less computational time and low power consumption compared to existing authentication protocols such as the EAKES6Lo and SAKES. LAUP is evaluated using real‐time testbed and achieved less computational time, which is supportive of our simulated results.

Summary Anonymous authentication technique receives wide attention in recent years since it can protect users' privacy. Anonymous web browsing refers to utilization of the World Wide Web that hides a user's personally identifiable information from the websites visited. Even if a user can hide the IP address and other physical information with anonymity programs such as Tor, the web server can always monitor the user on the basis of the identity. In this paper, we firstly give an overview and cryptanalysis on the protocol of Yang et al and point out the security weaknesses of their protocol. Then, we propose a new authentication protocol for anonymous web browsing. In the proposed protocol, we take the advantages of a pseudo identity mechanism and an identity‐based elliptic curve cryptography algorithm to achieve user anonymity, robust security, and high efficiency. The result of security analysis and performance evaluation indicate the feasibility and practicality of our proposed anonymous authentication protocol.

The constrained application protocol (CoAP) is a specially designed Web transfer protocol for use with constrained nodes and low-power networks. The widely available CoAP implementations have failed to validate the remote CoAP clients. Each CoAP client generates a random source port number when communicating with the CoAP server. However, we observe that in such implementations it is difficult to distinguish the regular packet and the malicious packet, opening a door for a potential off-path attack. The off-path attack is considered a weak attack on a constrained network and has received a less attention from the research community. However, the consequences resulting from such an attack cannot be ignored in practice. In this article, we exploit the combination of IP spoofing vulnerability and the remote server access support of CoAP is to be launch an off-path attack. The attacker injects a fake request message to change the credentials of the 6LoWPAN smart door keypad lock system. This creates a request spoofing vulnerability in CoAP, and the attacker exploits this vulnerability to gain full access to the system. Through our implementation, we demonstrated the feasibility of the attack scenario on the 6LoWPAN-CoAP network using smart door keypad lock. We proposed a machine learning (ML)-based approach to mitigate such attacks. To the best of our knowledge, we believe that this is the first article to analyze the remote CoAP server access support and request spoofing vulnerability of CoAP to launch an off-path attack and demonstrate how an ML-based approach can be deployed to prevent such attacks.

Resource constrained sensing devices are being used widely to build and deploy self-organizing wireless sensor networks for a variety of critical applications such as smart cities, smart health, precision agriculture and industrial control systems. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. A Data Stream Manager (DSM) at the server collects the data streams (often called big data) to perform real time analysis and decision-making for these critical applications. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to assure the trustworthiness of the collected data so that any decisions are made on the processing of correct data. Assuring high data trustworthiness requires that the system satisfies two key security properties: confidentiality and integrity. To ensure the confidentiality of collected data, we need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity of emerging applications or the sensed data types or the sensing devices. For example, a temperature in a precision agriculture application may not be as sensitive as monitored data in smart health. Providing multilevel data confidentiality along with data integrity for big sensing data streams in the context of near real time analytics is a challenging problem. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality and data integrity. Our method is based on two key concepts: common shared keys that are initialized and updated by DSM without requiring retransmission, and a seamless key refreshment process without interrupting the data stream encryption/decryption. Theoretical analyses and experimental results of our SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams.

Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produce security-centric descriptions by performing program analysis. However, the generated text does not always address users’ concerns as they are generally too technical to be understood by ordinary users. Moreover, different users have varied linguistic preferences that do not match the text. Motivated by this challenge, we develop an innovative scheme to help users avoid malware and privacy-breaching apps by generating security descriptions that explain the privacy and security related aspects of an Android app in clear and understandable terms. We implement a prototype system, PERSCRIPTION, to generate personalised security-centric descriptions that automatically learn users’ security concerns and linguistic preferences to produce user-oriented descriptions. We evaluate our scheme through experiments and user studies. The results clearly demonstrate the improvement on readability and users’ security awareness of PERSCRIPTION’s descriptions compared to existing description generators.

Individuals, organizations, and government agencies are increasingly relying on Internet-enabled collaboration among distributed teams of humans, computer applications, and autonomous entities such as robots to develop products and deliver services. Technology trends in areas such as networking, data analytics, and distributed systems have significantly shifted the landscape of Internet-based collaborative tools and services. This particular special issue contains articles describing novel and innovative Internet-based collaborative technologies that leverage emerging technologies and enable seamless collaboration.

Cloud infrastructure providers are offering consumers a wide range of resource and contract options to choose from, yet most elasticity management solutions are incapable of leveraging this to optimize the cost and performance of cloudhosted applications. To address this problem, in this paper, we propose a novel resource scaling approach that exploits both resource and contract heterogeneity to achieve optimal resource allocations and better cost control. We model resource allocation as an Unbounded Knapsack Problem, and resource scaling as an one-step ahead resource allocation problem. Based on this, we present two scaling strategies, namely delta scale optimization and full scale optimization. Delta scale optimization supports the traditional notion of scaling resources horizontally, i.e., it computes an optimal allocation (or deallocation) of resources to increase (or decrease) the total compute capacity based on the current allocation and the forecast application workload. Full scale optimization, on the other hand, supports the notion of cost-optimal resource rescaling, i.e., the simultaneous allocation and deallocation of resources to meet the forecast workload irrespective of the decision to increase, decrease or maintain capacity. Both strategies provide users greater flexibility in managing trade offs between cost and performance. We motivate our research work by using a realistic and non-trivial scenario of resource scaling for a cloud-hosted IoT platform and use simple use cases to illustrate the benefit of our proposed approach.

Symmetric Searchable Encryption (SSE) has received wide attention due to its practical application in searching on encrypted data. Beyond search, data addition and deletion are also supported in dynamic SSE schemes. Unfortunately, these update operations leak some information of updated data. To address this issue, forward-secure SSE is actively explored to protect the relations of newly updated data and previously searched keywords. On the contrary, little work has been done in backward security, which enforces that search should not reveal information of deleted data. In this paper, we propose the first practical and non-interactive backward-secure SSE scheme. In particular, we introduce a new form of symmetric encryption, named symmetric puncturable encryption (SPE), and construct a generic primitive from simple cryptographic tools. Based on this primitive, we then present a backward-secure SSE scheme that can revoke a server's searching ability on deleted data. We instantiate our scheme with a practical puncturable pseudorandom function and implement it on a large dataset. The experimental results demonstrate its efficiency and scalability. Compared to the state-of-the-art, our scheme achieves a speedup of almost 50x in search latency, and a saving of 62% in server storage consumption.

Resilience is an important property of distributed cyber defence systems operating in complex, adversarial environments. While resilience can be realized through self-management, implementing distributed self-management poses several significant challenges. In this paper, we identify some of these challenges and present our initial work on an approach for cyber resilience called AWaRE. The novelty of AWaRE lies in the use of a conceptual, state-space-based design and reconstitution framework, combined with run-time models and distributed constraint satisfaction/optimization techniques for decision-making and coordination of system re-configurations. The run-time models are generated via an expressive domain-specific language enabling component, constraint and agent modeling as well as constraint problem decomposition and architectural self-organization. We realize AWaRE via a concrete software framework, focusing specifically on autonomic self-* properties pertaining to distributed system configuration, deployment and reliable operation. We demonstrate the value of AWaRE in the context of a simple, cloud-based enterprise scenario.

With the ubiquitous presence of smart phones and the availability of easy-to-use applications, there is an increase in the number of online services. A growing number of people now search for information and interact online. They expect to see services available and accessible online. To meet citizens’ expectations, governments have also increased their online presence. However, information and services are not the only reasons people go online. People also build their social circle online, seeking support and empathy, looking for someone with whom they can talk and who can understand their situation and worries. Online communities (and social networks in general) have been shown to have the potential to provide social and emotional peer-support. Our work aimed at determining whether online communities could be deployed in the public administration domain, in particular to support people receiving welfare payments, with similar benefits. We hypothesized that an online community could provide such support to disadvantaged citizens. Toward testing this hypothesis, after a user requirements analysis and some preparatory work, we designed and developed an online community for a specific target group of welfare recipients, as a collaboration between CSIRO and the Australian Department of Human Services. The community was deployed for one year. In this paper, we briefly explain our aims and the work that went into preparing for the community. We introduce the portal and the support it offered. We then report our observations and findings about both the informational and emotional support participants received, through an analysis of the comments posted in the community, and whether this support was perceived as welcome and useful.

I am very happy to report that TSC has gained an Impact Factor (IF) of 3.520 and the 5-year IF of 4.245, both of which represent significant increases from the previous years. This further speaks to the global reputation of the journal and the amazing work done by the past EICs, all the current and past EB members, and reviewers - all of whom have volunteered their precious time despite their very busy schedule to support and contribute to the growth of this journal. I hope to count on your continued engagement for the future growth of this journal. Over this past year, several esteemed EB members have completed their terms of service to TSC after serving for several years. On behalf of the Services Computing community and the TSC EAB, I would like to thank the following Associate Editors who retired from TSC EB in 2017 for their invaluable service and contributions to the journal. Overall, I am very proud of the success that TSC has achieved in 2017. This would not have been possible without the continued support of the authors, readers, reviewers, TSC EAB, TSC EB, and the staff of IEEE and IEEE Computer Society. I look forward to exploring ways to further enhance the reputation and impact of our journal. I would love to hear your suggestions and comments, and I hope to have your continued support.

People always spend their time at a few important locations for various activities in groups during specific time slots, called place of activity (POA), e.g., resting at home among family members during night and working at office among colleagues during work time. Inferring such places is significant for not only the precise advertising on the commercial aspect but the identifying rallies or meetings among a group of people and tracking of the target individuals on the aspect of public security, e.g., locating and tracking suspected terrorists for anti-terrorist work. However, it is a challenge to map from big location data to places of activity due to the volume and complexity whilst giving rise to privacy concerns, e.g., personally important place mining. In the paper, a method for POA mining on big location data is proposed, named P-PAM, aiming at big data analytics and privacy concerns. We use a clustering algorithm to discover the place of activity, then adopt location entropy as reference of user diversity and take into account temporal variation, to infer place of activity. Further, robust privacy-preserving mechanisms under differential privacy are embedded into clustering results and location entropy evaluation that accesses to raw location data. We demonstrate the utility of our proposed approach with large-scale location datasets derived from geo-referenced social media. The experimental results suggest that the POA mining approach can successfully scale to big data scenarios whilst preserving individual user privacy.

With the explosive growth of location-aware devices and global adoption of social network applications, enormous volumes of spatiotemporal data are being produced. These can be perceived as gazetteers that record frequently visited locations, e.g. shopping malls and museums, and potentially more sensitive locations, e.g. an individual's home/work locations. Density-based clustering approaches are generally used for gazetteer discovery. However, existing clustering solutions are inefficient for big data scenarios and often disregard mobility features derived from trajectories data. Further, automated gazetteer discovery applications may cause privacy concerns. In this paper, we propose a sensitive gazetteer automated discovery approach based on Ω-cluster with robust privacy controls. The approach identifies sensitive gazetteers from massive trajectory data, with location entropy-based filtering used to reduce the number of uninteresting clusters whilst considering mobility features of trajectories. A parallelized solution is implemented to scale across the cloud using memory-oriented data processing solutions based upon Apache Spark. We embed this algorithm in a privacy-preserving mechanism and subsequently release sanitized gazetteers. Through extensive experiments using synthetic and real trajectory datasets from the location based social network (Twitter), we demonstrate the effectiveness and efficiency of our approach.

We introduce an efficient Key-Policy Attribute-Based Encryption (KP-ABE) scheme in prime order groups. Our scheme is semi-adaptively secure under the decisional linear assumption and supports a large universe of attributes and multi-use of attributes. Those properties are critical for real applications of KP-ABE schemes since they enable an efficient and flexible access control. Prior to our work, existing KP-ABE schemes with short ciphertexts were in composite order groups or utilized either Dual Pairing Vector Spaces (DPVS) or Dual System Groups (DSG) in prime order groups. However, those techniques brought an efficiency loss. In this work, we utilize a nested dual system encryption which is a variant of Waters' dual system encryption (Crypto' 09) to achieve semi-adaptively secure KP-ABE. As a result, we obtain a new scheme having better efficiency compared to existing schemes while it keeps a semi-adaptive security under the standard assumption. We implement our scheme and compare its efficiency with the previous best work.

Fuzzy extractor provides key generation from biometrics and other noisy data. The generated key is seamlessly usable for any cryptographic applications because its information entropy is sufficient for security. Biometric authentication offers natural and passwordless user authentication in various systems where fuzzy extractors can be used for biometric information security. Typically, a biometric system operates in two modes: verification and identification. However, existing fuzzy extractors does not support efficient user identification. In this paper, we propose a succinct fuzzy extractor scheme which enables efficient biometric identification as well as verification that it satisfies the security requirements. We show that the proposed scheme can be easily used in both verification and identification modes. To the best of our knowledge, we propose the first fuzzy extractor based biometric identification protocol. The proposed protocol is able to identify a user with constant computational cost rather than linear-time computation required by other fuzzy extractor schemes. We also provide security analysis of proposed schemes to show their security levels. The implementation shows that the performance of proposed identification protocol is constant and it is close to that of verification protocols.

Interactions among tweets, i.e., mentions, retweets, replies, are important factors contributing to the quality of topic derivation on Twitter. If applied correctly, the incorporation of tweet interactions can significantly improve the quality of topic derivation in comparison with approaches that are mainly based on the content similarity analysis. However, how interactions can be measured and integrated with content similarity for topic derivation remains a challenge. In previous work, the strength of tweet-to-tweet relationship has been computed by simply adding measures for content similarity, mentions, and reply-retweets. This simple linear addition does not accurately reflect the various impacts these factors have on tweet relationships. In order to address this issue, we propose a joint probability model that can effectively integrate the effects of the content similarity, mentions, and reply-retweets to measure the tweet relationship for the purpose of topic derivation. The proposed method is based on matrix factorization techniques, which enables a flexible implementation on a distributed system in an incremental manner. Experimental results show that the proposed model results in a significant improvement in the quality of topic derivation over existing methods.

People are suffering from a range of risks in the ubiquitous networks of current world, such as rumours spreading in social networks, computer viruses propagating throughout the Internet and unexpected failures happened in Smart grids. We usually monitor only a few users of detecting various risks due to the resource constraints and privacy protection. This leads to a critical problem to detect compromised users who are out of surveillance. In this paper, we propose a risk assessment method to address this problem. The aim is to assess the security status of unmonitored users according to the limited information collected from monitored users in networks. There are two innovative techniques developed: First, we identify the source of risk propagation by inversely disseminating risks from the influenced (by rumours) or infected (by viruses) monitored users. We show a new finding that the ones who synchronously receive the risk copies from all monitored users are most likely to be the sources. Second, we propose a microscopic mathematical model to present the risk propagation from the exposed sources. This model forms a discriminant to classify the compromised users from others. For evaluations, we collect three real networks on which we launch simulated risk propagation and then sample the status of monitored users. The experiment results show that our method is effective and the result of risk assessment well matches the real status of the unmonitored users.

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

A wide array of clouds have been built and adopted by business and research communities. Similarly, many research communities use workflow environments and tools such as Taverna and Galaxy to model the execution of tasks to support and expedite the reenactment of complex processes, and ultimately support the repeatability of science. As a result, a number of systems that integrate clouds with these workflow systems have emerged including CloudMap, CloudMan, Galaxy cloud, BioBlend, etc. Though these systems have proven to be successful in service and data integration, they have not dealt with the data security issue inherent in cloud-based systems with the outsourced models of infrastructure provisioning. For many domains, e.g., health, this poses serious challenges regarding the adoption of cloud infrastructures. Yet such domains also have much to gain from clouds especially given the explosion of genomics data and opportunities for personalized medicine in the big data era. This paper addresses this problem by presenting a trusted storage cloud for scientific workflows, called TruXy. The paper describes the TruXy architecture, the corresponding protocols and illustrates the adoption of TruXy to support collaborative bioinformatics research in endocrine genomics. A range of experiments have been performed to measure the performance of TruXy for processing of exome data sets on individuals with a rare genetic disorders: disorders of sex development (DSD). Our results show that the performance of TruXy is comparable to that of using a standalone workflow tool and that it can handle the big data security challenges required.

Applications in risk-critical domains such as emergency management and industrial control systems need near-real-time stream data processing in large-scale sensing networks. The key problem is how to ensure online end-to-end security (e.g., confidentiality, integrity, and authenticity) of data streams for such applications. We refer to this as an online security verification problem. Existing data security solutions cannot be applied in such applications as they cannot deal with data streams with high-volume and high-velocity data in real time. They introduce a significant buffering delay during security verification, resulting in a requirement for a large buffer size for the stream processing server. To address this problem, we propose a Dynamic Key-Length-Based Security Framework (DLSeF) based on a shared key derived from synchronized prime numbers; the key is dynamically updated at short intervals to thwart potential attacks to ensure end-to-end security. Theoretical analyses and experimental results of the DLSeF framework show that it can significantly improve the efficiency of processing stream data by reducing the security verification time and buffer usage without compromising security.

Hybrid clouds have gained popularity in recent times in a variety of organizations due to their ability to provide additional capacity in a public cloud, to augment private cloud capacity, when it is needed. However, scheduling distributed applications' jobs (e.g, workflow tasks) on hybrid cloud resources introduces new challenges. One key problem is the danger of exposing private data and jobs in a third-party public cloud infrastructure, for example in healthcare applications. In this article, we tackle the problem of designing workflow scheduling algorithms to meet customers' deadlines, while not compromising data and task privacy requirements. Our work is different from most studies on workflow scheduling where the main goal is to achieve a balance between desirable, yet incompatible constraints, such as meeting the deadline and/or minimizing the execution time. Although many others have addressed the trade-off between cost and time, or privacy and cost, their work still suffers from an insufficient consideration of the trade-off between privacy and time. To address such shortcomings in the literature, we present a new SaaS scheduling broker composed of MPHC-P1, MPHCP2, and MPHC-P3 policies to preserve privacy while scheduling the workflows' tasks under customers' deadlines. We evaluated our approach using real workflows running on a VMware based hybrid cloud. Results demonstrate that under our scheduling policies, MPHC-P2 and MPHC-P3 are promising in time-critical scenarios by reducing the total cost by 10-20 percent compared to alternatives. Overall, results show that our approach is efficient in reducing the cost of executing workflows while satisfying both their privacy and deadline constraints.

The Internet of Things (IoT) provides transparent and seamless incorporation of heterogeneous and different end systems. It has been widely used in many applications including smart cities such as public water system, power grid, water management, and vehicle traffic control system. In these smart city applications, a large number of IoT devices are deployed that can sense, communicate, compute, and potentially actuate. The uninterrupted and accurate functioning of these devices are critical to smart city applications as crucial decisions will be made based on the data received. One of the challenging tasks is to assure the authenticity of the devices so that we can rely on the decision making process with a very high confidence. One of the characteristics of IoT devices deployed in such applications is that they have limited battery power. A challenge is to design a secure mutual authentication protocol which is affordable to resource constrained devices. In this paper, we propose a lightweight mutual authentication protocol based on a novel public key encryption scheme for smart city applications. The proposed protocol takes a balance between the efficiency and communication cost without sacrificing the security. We evaluate the performance of our protocol in software and hardware environments. On the same security level, our protocol performance is significantly better than existing RSA and ECC based protocols. We also provide security analysis of the proposed encryption scheme and the mutual authentication protocol.

Provenance is information about the origin and creation of data. In data science and engineering, such information is useful and sometimes even critical. In spite of that, provenance for big data is under-explored due to the challenges from the `Vs' of big data. In data analytics, users need to query history, reproduce intermediate or final results, tune models, and adjust parameters in runtime for making data-driven decisions. In addition, users need to evaluate data and pipeline trustworthiness. Towards realising these functionalities for big data provenance, we propose a solution, called LogProv, which needs to renovate data pipelines or even some of big data software infrastructure to generate structured logs for pipeline events, and then stores data and logs separately. The data are explicitly linked to the logs, which implicitly record pipeline semantics. Semantic information can be retrieved from the logs easily since the logs are well defined and structured beforehand. We implemented LogProv in Apache Pig, and adopted ElasticSearch to provide query service. In this paper LogProv is evaluated in a Hadoop ecosystem hosted by a cloud and empirically case-studied. The results show that LogProv is efficient since the performance overhead is no more than 10%, the query can be responded within 1 second, the trustworthiness is marked clearly, and there is no impact on the data processing logic of original pipelines.

Unprecedented volumes of location-based information have been produced as a result of the widespread adoption of social network applications and GPS-enabled devices and sensors. Publication of such location data can provide valuable resources for researchers and government agencies in applications ranging from near real-time population-wide health monitoring to planning for future cities. However, such data hold personally identifying information, which gives rise to many privacy issues. There is thus a pressing need for ways to restrict this inherently identifying location-related information, however ideally we would like to preserve the utility of the data. Importantly, any such solution has to be scalable to large population-wide data scenarios. To tackle this, we introduce a novel differentially private hierarchical location sanitization (DPHLS) approach based on the concept “(α, r)-dataset” implemented through a Variable Order Mobility Markov Model (VO3M). We show how this system allows individual locations in personal trajectories to be protected using selection and frequency perturbation mechanisms using the “(α, r)-dataset”, leveraging past (published) location histories to obfuscate the user location in a flexible and controllable manner. The effectiveness and efficiency of the proposed solution is evaluated through the big data experiments that have been carried out using an OpenStack-based Cloud and Apache Sparkbased platform utilising large-scale social media trajectories. The experimental results suggest that the privacy publication algorithm can successfully scale to big data scenarios whilst retaining the utility of the datasets (trajectories) and preserving individual user privacy.

Advanced data analytics have become an integral part of a number of eScience initiatives including the many challenges facing the urban sciences. Understanding the movement of people and their spatial trajectorits would greatly aid the development of policies for sustainable urban living including urban traffic analysis and smart city management. Due to the widespread popularity of mobile devices with location-aware capabilities and the extensive prevalence of location-based social networks, citizens' movements and their trajectories are being produced and gathered at an unprecedented rate. In this context, there are two fundamental issues that need to be addressed: trajectory data hold private information of citizens that require privacy preserving solutions for data release and analysis, and the heterogeneous nature of the trajectory data makes it hard to effectively measure their similarity, which is fundamental to trajectory analysis. In this paper, we address these challenges by proposing an innovative private trajectories calibration model that not only guarantees the privacy of citizens, but also increases the utility. We have conducted comprehensive experiments using real-life user trajectories extracted from Twitter data. The results reveal the effectiveness and efficiency of the proposed approach, which is also reported in this paper.

Cloud computing has been widely adopted by enterprises in their business application in the last decade. Although Cloud computing was incepted with business computing needs in mind, it is increasingly being considered by scientific communities. In recent times, a number of science Clouds have been built for specific research communities. Many research communities use workflow environments and tools such as Taverna and Galaxy to model the execution of tasks to support and expedite the re-enactment of complex processes, and ultimately support the repeatability of science. As a result, a number of systems that integrate Clouds with these workflow systems have emerged including CloudMap, CloudMan, Galaxy Cloud, BioBlend. Whilst there has been many concerns about the capability of virtualized Cloud resources to meet the needs of scientific computation, recent results have shown that the performance of public Clouds are comparable with HPC systems for notable applications. In recent times, a combination of science Cloud and public Cloud has also been used for scientific applications. Given that a large number of scientific applications and research communities use automated process controls, usually in the form of workflows, there is a need to facilitate workflow execution on scalable platforms, such as public Clouds. Along with generic Cloud-based workflow systems, many specific Cloud-based workflow systems have been developed for science applications such as life sciences, and astronomy.

Cloud storage services have become ubiquitous. A large number of individuals and organizations are using them to store and share data, taking the benefits of mobility and affordability offered by these services. However, secure management of data in cloud storage services, more specifically supporting multi-party sharing in the context of a collaboration, is a challenging problem. The problem is further exacerbated if the data owner does not have any trust on the cloud storage providers and the data need regular updates from collaborating parties. A number of cryptographically enforced secure cloud storage solutions have been proposed to address this problem. One of the key issues with these solutions is the revocation of access to data for invalid users without moving the data (in the era of big data) and relying on the cloud service providers. In this paper, we introduce a cloud storage system that offers cryptographically enforced security. In contrast to other cryptographically protected cloud storage systems, our system supports a fine-grained access control mechanism and allows flexible revocations of invalid users without moving the data and relying on the cloud service providers. Our system employs an attribute-based encryption technique to support a complex access structure that allows a user to define human readable access policies to the data in the cloud storage. In addition, our system supports a flexible revocation scheme that can revoke invalid users directly by updating the revoked users’ list or indirectly by updating an epoch counter. The system administrator can choose one of these options flexibly depending on the needs. Our system also allows authorized users to update the encrypted data, and any users accessing such updated data in future can verify whether the data are modified by authorized users.

The Internet of Things (IoT) is the latest Internet evolution that incorporates a diverse range of things such as sensors, actuators, and services deployed by different organizations and individuals to support a variety of applications. The information captured by IoT present an unprecedented opportunity to solve large-scale problems in those application domains to deliver services; example applications include precision agriculture, environment monitoring, smart health, smart manufacturing, and smart cities. Like all other Internet based services in the past, IoT-based services are also being developed and deployed without security consideration. By nature, IoT devices and services are vulnerable to malicious cyber threats as they cannot be given the same protection that is received by enterprise services within an enterprise perimeter. While IoT services will play an important role in our daily life resulting in improved productivity and quality of life, the trend has also “encouraged” cyber-exploitation and evolution and diversification of malicious cyber threats. Hence, there is a need for coordinated efforts from the research community to address resulting concerns, such as those presented in this special section. Several potential research topics are also identified in this special section.

Secure cloud storage solutions such as Trust Store, Sec Cloud, HPI Secure, and Twin Cloud have primarily focused on securing persistent data while storing it in public cloud services. Though data sharing has been recognized as an important security feature, these storage solutions mostly focus on three key properties: confidentiality, integrity and availability. Modern enterprise applications demand data is able to be shared within or across organizations. The challenge is how to securely share data in public clouds without increasing data movement and computation costs. This problem has been addressed in recent times by utilizing or developing new data encryption techniques such as identity-based encryption, attribute-based encryption and proxy-re-encryption. However, these techniques suffer from scalability and flexibility problems when dealing with big data and support for dynamic access control rules. This paper presents a novel architecture and corresponding protocols to provide secure sharing of documents on public cloud services: Cloud Docs. This system uses AES for data encryption to achieve scalability and supports identity based access control rules using private-public key pairs to provide flexibility.

Although content sharing provides many benefits, content owners lose full control of their content once they are given away. Existing solutions provide limited capabilities of content access control as they are vendor-specific, non-structured and non-flexible. In this paper, we present an open and flexible software solution called SelfProtect Object (SPO). SPO bundles content and policy files in an object that can protect its contents by itself anywhere and anytime. Our policy is based on XACML, a generic policy language allowing fine-grain access with rules and conditions. We also design and implement a prototype of SPO and demonstrate its capability through examples. Our solution is flexible to express a variety of access control rules and open to integrate into different applications on different platforms.

Abstract Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.

A note on resource orchestration for cloud computingWelcome to the special issue of Concurrency and Computation: Practice and Experience (CCPE) journal.This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in the areas of orchestrating cloud resources, composing new cloud services from existing ones, increasing energy efficiency via cloud resource orchestration, and developing cloud-based image processing solutions.Over the past few years, cloud computing [1-4] has emerged as the latest and most dominant utility computing solution offering both hardware and software resources as virtualization-enabled services.Cloud computing providers such as Amazon Web Services and Microsoft Azure currently provide application owners the option of deploying their applications over a network of a virtually infinite resource pool with practically no up-front capital investment and with operating cost proportional to the actual use (i.e., implementing a pay-as-you-go model).An increasing number of cloud vendors offer information and communication technology (ICT) resources such as hardware (CPUs, GPUs, storage, and networks), software infrastructure (e.g., databases, webservers, stream-processing systems, and data-mining packages), and collaboration/communication applications (e.g., email, video on demand, and social networks) as infrastructure as a service (IAAS), platform as a service (PAAS), and software as a service (SAAS), respectively.This approach allows enterprises to easily, cost effectively, and reliably offer business services that are supported by computing and software resources that are provided and maintained by IAAS, PAAS, and SAAS providers.This makes cloud computing attractive to especially small and medium size enterprises (SMEs), as it allows them to focus more on their core business and less on ICT infrastructure.One of the fundamental issues in exploiting cloud computing in this fashion is developing better Resource Orchestration (RO) [1-5] techniques and programming frameworks.More specifically, Resource Orchestration (RO) is 'the set of operations that cloud providers (e.g., AWS) and application owners (e.g., Netflix) undertake (either manually or automatically via computer programs) for selecting, deploying, monitoring, and dynamically controlling configuration of hardware and software resources as a system of QoS assured components that can be seamlessly delivered to end-users' [1].Since RO operations span across all layers of cloud computing stack [1], an overall goal of RO is to ensure successful hosting and delivery of applications (SAAS) by managing the fulfillment of the QoS objectives of both the application owners (e.g., maximize availability, maximize throughput, minimize latency, and avoid overloading) and the Cloud resource providers (e.g., maximize utilization, maximize energy efficiency, and maximize profit).One of the main complexities in Cloud resource management is that Cloud resources are typically identified by unique functional specifications, and then evaluated via their Quality of Service (QoS) properties.However, in practice, each resource may have multiple unique functional specifications that enable serving diverse user needs.For example, a song retrieval application is a Cloud resource that can be identified and enacted by the name of a song or via its lyrics, as users do may not know or remember the names of all song they want to find.The paper titled 'A Service Evaluation Method for Cross-cloud Service Choreography' [6] addresses this challenge by proposing a multifunctional specification solution for cross-cloud service choreography.This solution is based on a mixed integer programming model for multifunctional specification/identification that decomposes global resource and application constraints into local constraints.This allows constraint evaluations to be performed for cross-cloud service choreography.Experimental verification of this approach is also provided.Provisioning cloud resources requires providers and consumers to reach an agreement on the service usage terms and conditions.Such agreements are captured as Service Level Agreements (SLAs).The paper titled 'AutoSLAM -A Policy-based Framework for Automated SLA Establishment in Cloud

Cloud computing provides promising scalable IT infrastructure to support various processing of a variety of big data applications in sectors such as healthcare and business. Data sets like electronic health records in such applications often contain privacy-sensitive information, which brings about privacy concerns potentially if the information is released or shared to third-parties in cloud. A practical and widely-adopted technique for data privacy preservation is to anonymize data via generalization to satisfy a given privacy model. However, most existing privacy preserving approaches tailored to small-scale data sets often fall short when encountering big data, due to their insufficiency or poor scalability. In this paper, we investigate the local-recoding problem for big data anonymization against proximity privacy breaches and attempt to identify a scalable solution to this problem. Specifically, we present a proximity privacy model with allowing semantic proximity of sensitive values and multiple sensitive attributes, and model the problem of local recoding as a proximity-aware clustering problem. A scalable two-phase clustering approach consisting of a t-ancestors clustering (similar to k-means) algorithm and a proximity-aware agglomerative clustering algorithm is proposed to address the above problem. We design the algorithms with MapReduce to gain high scalability by performing data-parallel computation in cloud. Extensive experiments on real-life data sets demonstrate that our approach significantly improves the capability of defending the proximity privacy breaches, the scalability and the time-efficiency of local-recoding anonymization over existing approaches.

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

In the paradigm of service oriented science, scientific computing applications and data are all wrapped as web accessible services. Scientific workflows further integrate these services to answer complex research questions. However, our earlier study conducted on myExperiment has revealed that although the sharing of service-based capabilities opens a gateway to resource reuse, in practice, the degree of reuse is very low. This finding has motivated us to propose ServiceMap to provide navigation facility through the network of services to facilitate the design and development of scientific workflows. This paper proposes ReputationNet as an enhancement of ServiceMap, to incorporate the often-ignored reputation aspects of services/workflows and their publishers, in order to offer better service and workflow recommendations. We have developed a novel model to reflect the reputation of e-Science services/workflows, and developed heuristic algorithms to provide service recommendations based on reputations. Experiments on myExperiment have illustrated a strong positive correlation (with Pearson correlation coefficient 0.82) between the reputation scores computed and the actual performance (i.e. usage frequency) of the services/workflows, which demonstrates the effectiveness of our approach.

The trend towards Cloud computing infrastructure has increased the need for new methods that allow data owners to share their data with others securely taking into account the needs of multiple stakeholders. The data owner should be able to share confidential data while delegating much of the burden of access control management to the Cloud and trusted enterprises. The lack of such methods to enhance privacy and security may hinder the growth of cloud computing. In particular, there is a growing need to better manage security keys of data shared in the Cloud. BYOD provides a first step to enabling secure and efficient key management, however, the data owner cannot guarantee that the data consumers device itself is secure. Furthermore, in current methods the data owner cannot revoke a particular data consumer or group efficiently. In this paper, we address these issues by incorporating a hardware-based Trusted Platform Module (TPM) mechanism called the Trusted Extension Device (TED) together with our security model and protocol to allow stronger privacy of data compared to software-based security protocols. We demonstrate the concept of using TED for stronger protection and management of cryptographic keys and how our secure data sharing protocol will allow a data owner (e.g, author) to securely store data via untrusted Cloud services. Our work prevents keys to be stolen by outsiders and/or dishonest authorised consumers, thus making it particularly attractive to be implemented in a real-world scenario.

Organizations overcome resource shortages by utilizing the multiple services of cloud providers. This leads to sharing resources among various public and private clouds in order to improve the performance while executing the organization's complex workflow systems. Executing multiple workflows in such a hybrid environment needs an effective mapping between workflow's tasks and cloud resources that considers the trade-off between budget and time. There is also a challenge when organizations are forced to deploy workflow's tasks on public resources to execute the tasks before their requested deadlines without violating customers' privacy. In recent years, several online and static approaches were presented to schedule single or multiple workflows considering deadline and budget in cloud environments. However, these studies neglect the privacy constraint along with other SLAs such as deadline and budget. In this paper, we present two online algorithms to schedule multiple workflows under deadline and privacy constraints, while considering the dynamic nature of hybrid cloud environment. The proposed algorithms were evaluated with a series of simulation as well as real experiments using real-life privacy constrained healthcare workflows. Our two algorithms use different methods to rank the tasks: one utilises a novel technique for ranking, the other uses a similar approach to current existing studies. Results show that the novel approach outperforms the current existing ranking methods.

The growing popularity of Health Social Networking sites has a tremendous impact on people's health related experiences. However, without any quality filtering, there could be a detrimental effect on the users' health. Trust-based techniques have been identified as effective methods to filter the information for recommendation systems. This research focuses on dental care related social networks and recommendation systems. Trust is critical when choosing a dental care provider due to the invasive nature of the treatment. Surprisingly, current dental care recommendation systems do not use trust-based techniques, and most of them are simple reviews and ratings sites. This research aims at improving dental care recommendation systems by proposing a new framework, taking trust into account. It derives trust from both users' social networks and from existing crowdsourced information on dental care. Such a framework could be used for other healthcare recommendation systems where trust is of major importance.

Gamification, the idea of inserting game dynamics into portals or social networks, has recently evolved as an approach to encourage active participation in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. We decided to introduce gamification in an online community that we designed and developed with the Australian Government's Department of Human Services to support welfare recipients transitioning from one payment to another. We first defined a formal model of gamification and a gamification design process. In instantiating our model to the online community, we realised that our context applied a number of constraints on the gamification elements that could be introduced. In this paper, we outline the design and implementation of a gamification model for online communities and its instantiation into our context, with its specific requirements. While we cannot comment on the success of gamification to drive user engagement in our context (for lack of the possibility of a controlled experiment), we found our implementation of badges-based gamification a helpful way to provide a useful abstraction on the life of the community, providing feedback enabling us to monitor and analyze the community. We thus show how feedback provided by such gamification data has a potential to be useful to community providers to better understand the community needs and addressing them appropriately to maintain a level of engagement in the community.

The articles in this special issue focus on the use of cloud computing for social computing applications. Currently, two complimentary Internet based research areas are emerging: social computing and cloud computing. On the one hand, social computing empowers individual users with relatively low technological sophistication to use the web to engage in social interactions, contribute their expertise and share their content, experiences and opinions. On the other hand, cloud computing offers everyone sophisticated computing infrastructures and resources as utilities, so that individual users with relatively low computing knowledge can have at their disposal a high performing computing infrastructure with little investment. Together, these two complementary technological advances form the backbone of our digitized world, when coupled with the rise of sensors,mobile devices and the internet of things. Of course, they also face significant challenges. These articles look at each area and describe how they complement each other. The Social Web has become an important means of communication for everyone: individuals, organizations, and governments all use it to disseminate and share information, offer opinions and engage in discussions.

Mobile-based collaboration tools are increasingly used for communication and information sharing in delivering healthcare services that need collaboration across different geographical locations. Some of the typical features found in the collaboration tools include video conferencing facility, image

Telehealth services research projects often require to access a variety of data sources under different data access policies and privacy constrains. There is a need to link these clinical and administrative records from different data custodians and produce a research data for analytics. One of the

Recommendations to connect like-minded people can result in increased engagement amongst members of online communities, thus playing an important role in their sustainability. We have developed a suite of algorithms for friend recommendations using a social trust model called STrust. In STrust, the

Online support groups have proven to be very effective, in the health domain in particular, to provide their members with social, emotional and moral support. Drawing from this success, we are exploring the use of an online community to build a support group for parents currently in receipt of income support but needing to transition to find a job. Complementing the financial and informational support offered through existing welfare transition programs, our work aims to help people improve their prospect of finding a job to become financially self-sufficient and develop a support network to help them in their transition back to work. In this paper, we present the design of a journey of reflection and collaboration to encourage reflective thinking and foster relationships between community members.

Cloud computing has been developed in response to demand from companies seeking to deal with the execution cost of their complex distributed applications. Introducing the notion of hybrid clouds to the cloud computing paradigm brings out many challenges in resource provisioning for workflows. Hybrid clouds encounter the following two main obstacles in reaching their full potential: (1) customers' dissatisfaction due to the conflicting nature of the constraints (budget and deadline), and (2) exposure of customers' private data/jobs in hybrid cloud infrastructures. We believe that too little attention is paid to privacy issues for workflow scheduling under deadline constraint. Many algorithms exist to address the cost and time trade-off, however, they suffer from insufficient consideration of privacy. In this study, we present an algorithm that preserves privacy in scheduling of workflows, whilst still considering customers' deadlines and cost. We evaluated our approach using real workflows running on a private HTCondor-based hybrid cloud. Results were promising and demonstrated the efficiency of our approach in not only reducing the cost of executing workflows, but also satisfying both the privacy and deadline constraints of the submitted workflows.

SUMMARY Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to the current information technology industry and research communities while posing significant challenges on them as well. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever increasing data sets in big data applications. However, processing or sharing privacy‐sensitive data sets on cloud probably engenders severe privacy concerns because of multi‐tenancy. Data encryption and anonymization are two widely‐adopted ways to combat privacy breach. However, encryption is not suitable for data that are processed and shared frequently, and anonymizing big data and manage numerous anonymized data sets are still challenges for traditional anonymization approaches. As such, we propose a scalable and cost‐effective framework for privacy preservation over big data on cloud in this paper. The key idea of the framework is that it leverages cloud‐based MapReduce to conduct data anonymization and manage anonymous data sets, before releasing data to others. The framework provides a holistic conceptual foundation for privacy preservation over big data. Further, a corresponding proof‐of‐concept prototype system is implemented. Empirical evaluations demonstrate that scalable and cost‐effective framework for privacy preservation can anonymize large‐scale data sets and mange anonymous data sets in a highly flexible, scalable, efficient, and cost‐effective fashion. Copyright © 2013 John Wiley & Sons, Ltd.

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.

Business process simulation is an effective approach to many organisations to understand the progress of processes over a period of time and predict future activities, which can then be given as a feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to study the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete. This paper proposes the architecture for business process simulation and describes its prototype implementation. It lists several prediction techniques that have been implemented as part of the prototype system. The paper also describes an artificial neural network model that could be used for predicting the completion time of business processes that are constrained by the availability of resources.

Gamification has recently evolved as an approach to engage and encourage active participation of members in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. This pa

Cloud storage services provide highly scalable, available and pay-as-you-go storage space for individual and enterprise users. Cloud storage services are inherently insecure as the management of the data in the cloud storage is controlled by third parties beyond the reach of the data owner. To addre

The Cloud infrastructure services landscape advances steadily leaving users in the agony of choice. As a result, Cloud service dentification and discovery remains a hard problem due to different service descriptions, nonstandardised naming conventions and heterogeneous types and features of Cloud

Engagement serves as an important metrics in judging the success of any online social network. It is thus important to identify how specific mechanisms can help build engagement. In this work, we investigate how rewarding members for their honesty can offer better engagement and raise the level of cooperativeness in the society. We define a write-rate game to simulate activities in an online forum like setting and conduct experiments with it. Our results show that providing right reward for honest actions promotes engagement and cooperation in the society.

Service-centric solutions usually require rich context to fully deliver and better reflect on the underlying applications. We present a novel use of context in the form of customized user interface services with the concept of User Interface as a Service (UIaaS). UIaaS takes user profiles as input to generate context-aware interface services. Such interface services can be used as context to augment semantic services with contextual information leading to UIaaS as a Context (UIaaSaaC). The added serendipitous benefit of the proposed concept is that the composition of a customized user interface with the requested service is performed by the service composition engine, as is the case with any other services. We use a special-purpose language (called User Interface Description Language (UIDL)) to model and realize user interfaces as services. We use a real-life e-government application, human services delivery for the citizens, as a proof-of-concept. We also present a comprehensive evaluation of the proposed approach using a functional evaluation and a nonfunctional evaluation consisting of an end user usability test and expert usability reviews.

In this paper, we propose a secure cloud storage service architecture with the focus on Data Integrity as a Service (DIaaS) based on the principles of Service-Oriented Architecture and Web services. Our approach not only releases the burdens of data integrity management from a storage service by handling it through an independent third party data Integrity Management Service (IMS), but also reduces the security risk of the data stored in the storage services by checking the data integrity with the help of IMS. We define data integrity protocols for a number of different scenarios, and demonstrate the feasibility of the proposed architecture, service and protocols by implementing them on a public cloud, Amazon S3. We also study the impact of our proposed protocols on the performance of the storage service and show that the benefits of our approach outweigh the little penalty on the storage service performance.

Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be fo

Existing proposals to address trusted end-to-end communication tend to focus on attestation between client applications and their enterprise servers. These proposals have given little or no consideration to the vulnerabilities between client applications and their users, resulting in identity thefts or impersonator. To address this problem, we propose a biometric enabled portable Trusted Computing Platform (portable TCP). The proposed platform coordinates two stage authentication and subsequent integrity check to provide trusted pathways amongst users, applications, and enterprise servers. We describe the proposed solution and its implementation.

We propose a trust model for social networks with the aim of building trust communities that inspire members to share their experiences, feelings and opinions in an open and honest way without the fear of being judged. The unique feature of our model is that the trust value is derived from the social capital built in the social networks over a period of time. First, we introduce a framework for building trust communities using social trust. We then define an underlying social trust model, called STrust. Finally, we report the current state of the development and the analysis of the proposed trust model.

We propose a trust prediction model for service Web using the Hidden Markov Model (HMM). The proposed model uses a dynamic training scheme that incorporates temporal sensitivity by using a dynamic training pool. The concept of the dynamically updated training pool enables the system to absorb newest information from data. We present the implementation of the proposed model and corresponding algorithm and their evaluation on real life data.

The enormous amount of data generated in daily operations and the increasing demands for data accessibility across organizations are pushing individuals and organizations to outsource their data storage to cloud storage services. However, the security and the privacy of the outsourced data goes beyond the data owners' control. In this paper, we propose a service composition approach to preserve privacy for data stored in untrusted storage service. A virtual file system, called Trust Store, is prototyped to demonstrate this concept. It allows users utilize untrusted storage service provider with confidentiality and integrity of the data preserved. We deployed the prototype with Amazon S3 and evaluate its performance.

We propose and implement a cloud architecture of virtual TPMs. In this architecture, TPM instances can be obtained from the TPM cloud on demand. Hence, the TPM functionality is available for applications that do not have TPM chips in their local platforms. Moreover, users can access their keys and data in the same TPM instance even if they move to other platforms. The TPM functionality in cloud is easy to access for applications developed in different languages since cloud computing delivers services in standard protocols. The functionality of the TPM cloud is demonstrated by using it to implement the Needham-Schroeder public-key protocol for web authentication.

The mechanism of establishing trust in a computing platform is tightly coupled with the characteristics of a specific machine. This limits the portability and mobility of trust as demanded by many emerging applications that go beyond the organizational boundaries. In order to address this problem, we propose a trusted computing platform in a form of a USB device. First, we describe the design and implementation of the hardware and software architectures of the device. We then demonstrate the capabilities of the proposed device by developing a trusted application.

We propose a fuzzy trust management framework for the Service Web. The proposed framework supports a natural way of representing and querying consumers' perception on services. We describe the underlying models, algorithms and an implementation architecture, mainly focusing on the key features and contributions of the proposed framework.

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies.

This paper investigates the problem of reputation management in composite services. Our focus is on developing a method of distribution of reputation received by a composite service to its component services. The proposed method enables the composite service to provide a fair distribution of reputation values so that a component service is neither penalized nor awarded for the bad and good performances respectively, of other component services. Experiment results show that the proposed technique propagates the "fair share" of reputation from the composite service to its component services.

Electronic Contract (eContract) has been recognized as a good combination of technical specification and legal documentation for establishing and regulating virtual organizations built for dynamic collaborations. This paper presents a design and implementation of an eContract service with an aim of providing a trusted collaboration platform for collaborators. The implemented service uses Web services technologies to facilitate its collaborators not only to contribute resources in an eContract, but also to negotiate and instantiate them through eContract. This paper describes the interface and protocols for the eContract service. The architecture, interface and protocols designed for the service are demonstrated using an example of providing universal connectivity service for a telepresence application in the context of eResearch domain.

Dynamic collaborations involve contributed resources across the organisational boundaries that are subjected to different set of policies. The management of such resources for dynamic collaborations including negotiation, validation, instantiation and termination is difficult. Existing approaches for collaborations using Web Services such as WSLA are designed to deal with scenarios involving two parties: a service provider and a service consumer. These approaches do not scale well to multiparty nature of dynamic collaborations. To address this problem, we propose a framework for a language called Web Service Collaborative Context Definition Language for dynamic collaborations. The language itself has been defined using XML Schema and has been implemented in a dynamic collaboration platform.

Dynamic collaborations are the means by which a group of autonomous entities (possibly competing) collaborate to achieve a common objective by sharing resources that may be owned and managed both privately or jointly. Our focus in this paper is on controlling jointly owned shared data that is inherently ephemeral, as it does not exist outside the period of the collaboration. This paper explores the application of the ephemerizer concept as a means to control the access to shared collaboration data. Towards this, we first define a service-oriented architecture Anitya that enables the development of a third party service for managing ephemeral data in dynamic collaborations. We then extend the pair wise secure communication protocol used in the ephemerizer and propose three different multiparty secure group communication protocols for sharing collaboration data under the defined architecture. We also discuss the design, implementation, and evaluation of the architecture and protocols.

This paper first defines a format for capturing and distributing contributed resources called an eContract and then proposes an agreement protocol along with a simple distributed algorithm for negotiating the eContract in the context of dynamic collaborations. The insights gained from the analysis of the proposed negotiation algorithm lead us to the development of an efficient "message piggybacking" negotiation algorithm.

This paper discusses a novel implementation of a workflow engine that supports service-based applications. The applications are defined according to the GAT model, which is an event-based programming model using conditional guards to determine when both normal and exception-handling activities are to be executed. We propose implementation techniques for key features of GAT. These include implementing control flow based on the evaluation of guards, the management and distribution of events, and enforcing atomicity across the evaluation of guards and the execution of the corresponding activities. We have built an engine following this approach which uses available technologies to support translating GAT models into executable applications

The Web services and service-oriented architectures (SOA) vision by Helland, P. (2005) is about building large-scale distributed applications by composing coarse-grained autonomous services in a flexible architecture that can adapt to changing business requirements. These services interact by exchanging one-way messages through standardized message processing and transport protocols. This vision is being driven by economic imperatives for integration and automation across administrative and organizational boundaries. This paper presents a concise yet expressive model for service contracts to describe messaging behavior. The idea is simple: we use Boolean conditions to specify when a message can be sent and received, where the conditions refer only to other messages in the service contract - that is, conditions only refer to a service's externalized messaging state and not to internal state

This paper presents an edge-based semantic classification of sports video sequences. The paper presents an algorithm for edge detection, and illustrates the usage of edges for semantic analysis of video content. We first propose an algorithm for detecting edges within video frames directly on the MPEG format without a decompression process. The algorithm is based on a spatial-domain synthetic edge model, which is defined using interrelationship of two DCT edge features: horizontal and vertical. We then use a multi-step approach to classify video sequences into meaningful semantic segments such as "goal", "foul", and "crowd" in basketball games using the "edgeness" criteria. We then show how an audio feature ("whistles") can be used as a filter to enhance edge-based semantic classification.

Many new applications such as pervasive healthcare, pervasive infotainment and pervasive training, require relevant video segments (i.e. video highlights) to be delivered to a variety of devices operating under both wired and wireless platforms. This brings in a requirement of (a) way to describe the content as well as the environment such as device and network in which it is to be delivered, and (b) a method to adapt the content for the environment and the application. Maximizing user viewing experiences and minimizing resources required is the main aim of the adaptation process. This involves identifying relevant segments within a video and scaling down the video in different dimensions so that it can be delivered to the end users any time any where on any device. Towards this we have developed a prototype system for delivery of adaptive video (DAVE). In this paper, we explain how DAVE delivers video highlights in both wired and wireless platforms. Our focus in this paper is on content and environment description and content adaptation aspects of DAVE.

Our aim in this research is to make sense of scenes in video. We expect this will enable us to identify different scenes sharing the same semantic, even if they do not share any multimedia cues. Our approach is based on emergence, and involves classification and reasoning. We use patterns of cues to synthesize semantic classifications. These classifications need to be consistent with the observed cues, and suggest other elements that might be present. The suggestions might be based on sets of related patterns, ontology, or other knowledge bases. With an idea of what we are looking for, we can re-examine the scenes for multimedia elements, which support our hypothesis, or at least are not inconsistent. We expect that sufficiently detailed semantic descriptions can be generated, by cycling through these steps.

Recent content-based retrieval systems such as QBIC [7] and VisualSEEk [8] use low-level audio-visual features such as color, pan, zoom, and loudness for retrieval. However, users prefer to retrieve videos using high-level semantics based on their perception such as bright color and very loud sound. This results in a gap between what users would like and what systems can generate. This paper is an attempt to bridge this gap by mapping users’ perception (of semantic concepts) to lowlevel feature values. This paper proposes a model for providing high-level semantics for an audio feature that determines loudness. We first perform a pilot user study to capture the user perception of loudness level on a collection of audio clips of sound effects, and map them to five different semantic terms. We then describe how the loudness measure in MPEG-1 layer II audio files can be mapped to user perceived loudness. We then devise a fuzzy technique for retrieving audio/video clips from the collections using those semantic terms.

Advances in the media and entertainment industries, for example streaming audio and digital TV, present new challenges for managing large audio-visual collections. Efficient and effective retrieval from large content collections forms an important component of the business models for content holders and this is driving a need for research in audio-visual search and retrieval. Current content management systems support retrieval using low-level features, such as motion, colour, texture, beat and loudness. However, low-level features often have little meaning for the human users of these systems, who much prefer to identify content using high-level semantic descriptions or concepts. This creates a gap between the system and the user that must be bridged for these systems to be used effectively. The research presented in this paper describes our approach to bridging this gap in a specific content domain, sports video. Our approach is based on a number of automatic techniques for feature detection used in combination with heuristic rules determined through manual observations of sports footage. This has led to a set of models for interesting sporting events-goal segments-that have been implemented as part of an information retrieval system. The paper also presents results comparing output of the system against manually identified goals.

Content based retrieval systems have been developed for querying image data in which the users can pose queries based on visual properties such as color and texture. These systems, which have advanced the state of the art in image database systems, remarkably lack formal query languages. The traditional query languages are unable to capture the inherent fuzzy nature of the image data and content based querying. The fuzzy object query language (FOQL) presented in this paper addresses the need to support fuzzy values and fuzzy collections required for image databases. It can be used for defining schemas and high level concepts, and for querying image databases. It captures the inherent fuzzy nature of content based retrieval by keeping the query results fuzzy as against other query languages. The users can interactively refine their queries and high level concept definitions using recursive and named query definition constructs in FOQL. Being an extension of ODMG-OQL, FOQL can be easily mapped to ODMG-compliant visual query languages.

Multimedia database systems are essential for the effective and efficient use of large collections of image data. The aim of such systems is to enable retrieval of images based on their contents. As part of our research in this area, we are building a prototype content-based image retrieval system called CHITRA. This uses a four-level data model, and we have defined a fuzzy object query language (FOQL) for this system. This system enables retrieval based on high-level concepts, such as "retrieve images of mountains and sunset". A problem faced in this system is the processing of complex queries such as "retrieve all images that have a similar color histogram and a similar texture to the given example image". Such problems have attracted research attention in recent times. R. Fagin (1996) has given an algorithm for processing such queries and provided a probabilistic upper bound for the complexity of the algorithm (which has been implemented in IBM's Garlic project). In this paper, we provide a theoretical (probabilistic) analysis of the expected cost of this algorithm. We propose a new multi-step query processing algorithm and prove that it performs better than Fagin's algorithm in all cases. Our algorithm requires fewer database accesses. We have evaluated both algorithms against an image database of 1000 images on our CHITRA system. We have used both color histogram and Gabor texture features. Our analysis is presented and the reported experimental results validate our algorithm (which has a significant performance improvement).

No abstract available.