The widespread adoption of IoT devices and applications in smart homes has transformed the way we engage with our living environments. While enabling seamless automation and intelligent functionalities, interactions between different IoT applications, typically through trigger-condition-action (TCA) rules, may introduce new interaction threats due to rule conflicts, which sometimes lead to severe security and safety risks. However, existing detection and defense approaches often tackle specific threat categories in isolation, thereby failing to deliver a holistic perspective and robust, comprehensive protection. In this paper, we present InteractionShield, a novel framework that systematically detects and resolves rule conflicts by leveraging a logic analysis model based on event relations. The InteractionShield framework formalizes event relationships, detects event interferences, and classifies rule conflicts. It generates risk scores and conflict rankings to enable comprehensive conflict detection and risk assessment. To address the identified interaction threats, an optimization-based approach is employed to mitigate risks while maintaining system functionality. Evaluated on large-scale real-world IoT datasets, InteractionShield effectively enhances system reliability, offering a robust solution for detecting and resolving rule conflicts in smart environments.

The Vision Transformer (ViT) leverages the Transformer’s encoder to capture global information by dividing images into patches and achieves superior performance across various computer vision tasks. However, the self-attention mechanism of ViT captures the global context from the outset, overlooking the inherent relationships between neighboring pixels in images or videos. Transformers mainly focus on global information while ignoring the fine-grained local details. Consequently, ViT lacks inductive bias during image or video dataset training. In contrast, convolutional neural networks (CNNs), with their reliance on local filters, possess an inherent inductive bias, making them more efficient and quicker to converge than ViT with less data. In this paper, we present a lightweight Depth-Wise Convolution module as a shortcut in ViT models, bypassing entire Transformer blocks to ensure the models capture both local and global information with minimal overhead. Additionally, we introduce two architecture variants, allowing the Depth-Wise Convolution modules to be applied to multiple Transformer blocks for parameter savings, and incorporating independent parallel Depth-Wise Convolution modules with different kernels to enhance the acquisition of local information. The proposed approach significantly boosts the performance of ViT models on image classification, object detection, and instance segmentation by a large margin, especially on small datasets, as evaluated on CIFAR-10, CIFAR-100, Tiny-ImageNet and ImageNet for image classification, and COCO for object detection and instance segmentation. The source code can be accessed at https://github.com/ZTX-100/Efficient_ViT_with_DW .

The increasing use of the Internet of Things (IoT) technology has made our lives convenient, however, it also poses new security and privacy threats. In this work, we study a new type of privacy threat enabled by cross-app chains built among multiple seemingly benign IoT apps. We find that interactions among apps could leak privacy-sensitive information, e.g., users' identification, location and tracking, activity patterns, etc. To tackle this challenge, we introduce PrivacyGuard, which extracts cross-app chains in the form of trigger-condition-action rules and identifies the corresponding privacy leakage risk with an inference probability. PrivacyGuard supports a fine-grained categorization of privacy threats to generate detailed alerts about privacy leakages. We evaluated PrivacyGuard on a dataset with 2,101 SmartApps, 2,788 IFTTT rules, and 2,086 OpenHAB rules, respectively. The results show that PrivacyGuard could uncover hidden privacy leaks that existing studies fail to detect. For example, 7.67% chains constructed by two seemingly benign IoT apps could leak at least one type of privacy information, while over 80% of the leaks involved privacy information regarding Localization & Tracking and Activity Profiling.

Deep learning has recently attracted significant attention in the field of network intrusion detection. Despite a substantial number of efforts have been made, previous works struggle to comprehensively learn the features of network traffic, resulting in inconsistent performance across various environments and attacks. To address these limitation, this study presents SnifferDog, a novel network attack detection system that takes raw packets as input and rationally extracts and integrates heterogeneous features involved in packets, flows and topology. It formats the packets and flows concurrently to achieve a high-level throughout for feature learning. Then, a flow pretraining model consisting of a LSTM, a self-attention and cross-attention layers is developed to learn both sequential and nonsequential inter packet relation features as initial flow vectors. Subsequently, a node-to-node and a node-to-edge attention layers are implemented to enhance an inductive GNN model that dynamically embeds the flow-to-flow and flow-to-topology relation features into the flow vectors. The resulting flow vectors involve comprehensive information of packet-to-packet, flow-to-flow and flow-to-topology relations, enabling high detection performance. In-lab experiments across eight datasets from diverse environments demonstrate SnifferDog’s superior effectiveness over existing solutions. A scalable prototype deployed in our institute’s network achieves a false positive rate of only 0.08%, validating SnifferDog’s practicality in real-world scenarios.

Voice assistants (VAs), such as Amazon Alexa, are integrated with numerous smart home devices to process user requests using apps called skills. With their growing popularity, VAs also pose serious privacy concerns. Sensitive user data captured by VAs may be transmitted to third-party skills without users’ consent or knowledge about how their data is handled. Privacy policies are a standard medium to inform the users of the skills’ data practices. However, privacy policy compliance verification of such skills is challenging, since the source code is controlled by the skill developers, who can make arbitrary changes to the behaviors of the skill without being audited; hence, conventional defense mechanisms using static/dynamic code analysis can be easily evaded. In this paper, we present Eunomia, the first real-time privacy compliance firewall for Alexa skills. As the skills interact with the users, Eunomia hijacks and examines their communications from the skills to the users, and validates them against the published privacy policies that are parsed using a BERT-based policy analysis module. When non-compliant skill behaviors are detected, Eunomia stops the interaction and warns the user about the non-compliance. We evaluate Eunomia with 55,898 skills on Amazon skills store to demonstrate its effectiveness and to provide a privacy compliance landscape of Alexa skills.

With ChatGPT under the spotlight, utilizing large language models (LLMs) to assist academic writing has drawn a significant amount of debate in the community. In this paper, we aim to present a comprehensive study of the detectability of ChatGPT-generated content within the academic literature, particularly focusing on the abstracts of scientific papers, to offer holistic support for the future development of LLM applications and policies in academia. Specifically, we first present GPABench2, a benchmarking dataset of over 2.8 million comparative samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of scientific writing in computer science, physics, and humanities and social sciences. Second, we explore the methodology for detecting ChatGPT content. We start by examining the unsatisfactory performance of existing ChatGPT detecting tools and the challenges faced by human evaluators (including more than 240 researchers or students). We then test the hand-crafted linguistic features models as a baseline and develop a deep neural framework named CheckGPT to better capture the subtle and deep semantic and linguistic patterns in ChatGPT written literature. Last, we conduct comprehensive experiments to validate the proposed CheckGPT framework in each benchmarking task over different disciplines. To evaluate the detectability of ChatGPT content, we conduct extensive experiments on the transferability, prompt engineering, and robustness of CheckGPT.

Face recognition systems have been targeted by recent physical adversarial machine learning attacks, which attach or project visible patterns on adversaries' faces to trick backend FR models. While these attacks have demonstrated effectiveness in the literature, they often rely on visibly suspicious patterns, are susceptible to environmental noise, or exhibit limited success rates in practice. In this paper, we propose a novel physical adversarial attack against deep face recognition systems, namely Agile (Adversarial Glasses with Infrared LasEr). It generates adjustable, invisible laser perturbations and emits them into the camera CMOS to launch dodging and impersonation attacks against facial biometrics systems. To do so, we first theoretically model physical adversarial perturbations and convert them to the digital domain. The generated synthesized attack signals are utilized to guide real-world laser settings. Our experiments with real-world attackers and a benchmark face database show that Agile is highly effective in DoS, dodging, and impersonation attacks. More importantly, the candidate impersonation target and optimal attack settings identified by Agile's attack synthesis approach are highly consistent with real-world physical attack results. The grey-box and black-box evaluation against commercial FR models also confirms the effectiveness of the Agile attack.

Satellite modems are critical components in satellite communication networks. Especially, they determine the entire communication regime in traditional systems where the satellites only act as transparent relays. However, unlike satellites that are usually more isolated and better protected, satellite modems are accessible and susceptible to lower-cost attacks, potentially serving as a weak link in the chain of satellite communication security. We make the first attempt to shed light on satellite modem security. We first physically disassemble commodity satellite modems and systematically examine hardware and software modules. We perform a measurement study on the satellite modems that are exposed to the Internet. We identify 16 security vulnerabilities across three attack surfaces: satellite communication interface, ground network interface, and hardware. We further introduce AirSecAnalyzer, an automated security analyzer/fuzzer for the modems' satellite communication interface. Through comprehensive analysis and extensive experiments on 9 real-world satellite modems, we report 18 novel attacks that exploit the identified vulnerabilities. Our findings are expected to contribute as a valuable foundation for future research on the security of satellite modems and satellite communication networks.

In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish to disclose their private data samples either. Conventional privacy-preserving deep learning solutions ask the users to send encrypted samples to the model owners, who must handle the heavy lifting of ciphertext-domain computation with homomorphic encryption. In this paper, we present a novel solution, namely, PrivDNN, which (1) offloads the computation to the user side by sharing an encrypted deep learning model with them, (2) significantly improves the efficiency of DNN evaluation using partial DNN encryption, (3) ensures model accuracy and model privacy using a core neuron selection and encryption scheme. Experimental results show that PrivDNN reduces privacy-preserving DNN inference time and memory requirement by up to 97% while maintaining model performance and privacy. Codes can be found at https://github.com/LiangqinRen/PrivDNN

Ethics is an important criterion for security research. This work presents the current status and trends that security researchers have taken to address ethical concerns in their studies from a data perspective. In particular, we created a dataset of 3,756 papers published in three top-tier conferences between 2010 and 2022, among which 963 papers were identified with ethical concerns. With this dataset, we provided answers to three questions regarding the current practices and trends: (1) What is the landscape of ethical considerations in security research? For example, how many security research projects have raised ethical concerns in their studies, and which research areas are likely to cause ethical risks and concerns? (2) What are the current practices to address these ethical risks? And (3) What are the important factors impacting the ethical awareness of researchers?

This research addresses the challenge of conducting interpretable causal inference between a binary treatment and its resulting outcome when not all confounders are known. Confounders are factors that have an influence on both the treatment and the outcome. We relax the requirement of knowing all confounders under desired treatment, which we refer to as Selective Confounding, to enable causal inference in diverse real-world scenarios. Our proposed scheme is designed to work in situations where multiple decision-makers with different policies are involved and where there is a re-evaluation mechanism after the initial decision to ensure consistency. These assumptions are more practical to fulfill compared to the availability of all confounders under all treatments. To tackle the issue of Selective Confounding, we propose the use of dual-treatment samples. These samples allow us to employ two-step procedures, such as Regression Adjustment or Doubly-Robust, to learn counterfactual predictors. We provide both theoretical error bounds and empirical evidence of the effectiveness of our proposed scheme using synthetic and real-world child placement data. Furthermore, we introduce three evaluation methods specifically tailored to assess the performance in child placement scenarios. By emphasizing transparency and interpretability, our approach aims to provide decision-makers with a valuable tool. The source code repository of this work is located at https://github.com/sohaib730/CausalML.

With the development of computing and communication technologies, an extremely large amount of data has been collected, stored, utilized, and shared, while new security and privacy challenges arise. Existing access control mechanisms provided by big data platforms have limitations in granularity and expressiveness. In this article, we present SparkAC, a novel access control mechanism for secure data sharing and analysis in Spark. In particular, we first propose apurpose-aware access control(PAAC) model, which introduces new concepts ofdata processing purposeanddata operation purposeand an automatic purpose analysis algorithm that identifies purposes from data analytics operations and queries. Moreover, we develop a unified access control mechanism that implements PAAC model in two modules. GuardSpark++ supports structured data access control in Spark Catalyst and GuardDAG supports unstructured data access control in Spark core. Finally, we evaluate GuardSpark++ and GuardDAG with multiple data sources, applications, and data analytics engines. Experimental results show that SparkAC provides effective access control functionalities with very small (GuardSpark++) or medium (GuardDAG) performance overhead.

Bluetooth is a short-range wireless communication technology widely used by billions of personal computing, IoT, peripheral, and wearable devices. Bluetooth devices exchange commands and data, such as keyboard/mouse inputs, audio, and files, through a secure communication channel that is established through a pairing process. Due to the sensitivity of those commands and data, security mechanisms, such as encryption, authentication, and authorization, have been developed and adopted in the standards. Nevertheless, vulnerabilities continue to be discovered.

The wide adoption of deep neural networks (DNNs) in real-world applications raises increasing security concerns. Neural Trojans embedded in pre-trained neural networks are a harmful attack against the DNN model supply chain. They generate false outputs when certain stealthy triggers appear in the inputs. While data-poisoning attacks have been well studied in the literature, code-poisoning and model-poisoning backdoors only start to attract attention until recently. We present a novel model-poisoning neural Trojan, namely LoneNeuron, which responds to feature-domain patterns that transform into invisible, sample-specific, and polymorphic pixel-domain watermarks. With high attack specificity, LoneNeuron achieves a 100% attack success rate, while not affecting the main task performance. With LoneNeuron's unique watermark polymorphism property, the same feature-domain trigger is resolved to multiple watermarks in the pixel domain, which further improves watermark randomness, stealthiness, and resistance against Trojan detection. Extensive experiments show that LoneNeuron could escape state-of-the-art Trojan detectors. LoneNeuron~is also the first effective backdoor attack against vision transformers (ViTs).

Wireless power transfer (WPT) is one of the global highlight techniques with the rapid development of electric vehicles (EV). Currently, inductive power transfer (IPT) for EV charging faces the efficiency problem caused by the low saturation flux in the ferrite core bars. The nanocrystalline ribbon core is an advisable choice for the features of a high saturation flux and low power loss. In this paper, a double-D pads coupler with nanocrystalline core enhanced and ferrite TDK PC40 core will be detailed analyzed and compared regarding the magnetic field flux density, the air flux leakage around the vehicle body, the power loss distribution, and the magnetic shielding for wireless EV charging.

In the evasion attacks against deep neural networks (DNN), the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications. In this paper, we propose a novel multi-view adversarial image detector, namely Argos, based on a novel observation. That is, there exist two “souls” in an adversarial instance, i.e., the visually unchanged content, which corresponds to the true label, and the added invisible perturbation, which corresponds to the misclassified label. Such inconsistencies could be further amplified through an autoregressive generative approach that generates images with seed pixels selected from the original image, a selected label, and pixel distributions learned from the training data. The generated images (i.e., the “views”) will deviate significantly from the original one if the label is adversarial, demonstrating inconsistencies that Argos expects to detect. To this end, Argos first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree. Our experimental results show that Argos significantly outperforms two representative adversarial detectors in both detection accuracy and robustness against six well-known adversarial attacks. Code is available at: https://github.com/sohaib730/Argos-Adversarial_Detection

Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

Millions of Android applications are now deployed on billions of smartphones and tablet devices. An enormous amount of users' private data are being collected and made accessible to such apps. Extensive research efforts have been devoted to smartphone app security. In particular, the current practice of the app markets and app security scanners is to ensure that the requested permissions are consistent with the used permissions. On the other hand, mobile apps need to seek consent from users to approve various permissions to access user information. However, users often blindly accept permission requests and apps start to abuse this mechanism. For example, a flashlight app may obtain users' locations and send them out to the server. As long as a permission is requested by the app developer and approved by the users, the state-of-art detection mechanisms will treat it as benign. In this paper, we ask the question “are the permission requests really necessary?” The question is difficult to answer because it is hard to autonomously “comprehend” whether a permission is needed for the functionality of the app. We take the first attempt to tackle this challenge by comparing an app's permission requests with its peer apps, i.e., apps with similar functionalities. An app that requests/uses significantly more permissions than its peers is considered potentially malicious that will require further investigation. With this idea, we design a statistical approach to identify potentially excessive permission requests and evaluate it with apps from Play Store. Experiment results and case studies show that the proposed mechanism could effectively identify highly suspicious apps, which request many permissions that are not relevant to their functionalities.

Cryptography is essential for computer and network security. When cryptosystems are deployed in computing or communication systems, it is extremely critical to protect the cryptographic keys. In practice, keys are loaded into the memory as plaintext during cryptographic computations. Therefore, the keys are subject to memory disclosure attacks that read unauthorized data from RAM. Such attacks could be performed through software exploitations, such as OpenSSL Heartbleed, even when the integrity of the victim system's binaries is maintained. They could also be done through physical methods, such as cold-boot attacks, even if the system is free of software vulnerabilities. This paper presents Mimosa, to protect RSA private keys against both software-based and physical memory disclosure attacks. Mimosa uses hardware transactional memory (HTM) to ensure that (a) whenever a malicious thread other than Mimosa attempts to read the plaintext private key, the transaction aborts and all sensitive data are automatically cleared with hardware, due to the strong atomicity guarantee of HTM; and (b) all sensitive data, including private keys and intermediate states, appear as plaintext only within CPU-bound caches, and are never loaded to RAM chips. To the best of our knowledge, Mimosa is the first solution to use transactional memory to protect sensitive data against memory attacks. However, the fragility of TSX transactions introduces extra cache-clogging denial-of-service (DoS) threats, and attackers could sharply degrade the performance by concurrent memory-intensive tasks. To mitigate the DoS threats, we further partition an RSA private-key computation into multiple transactional parts by analyzing the distribution of aborts, while (sensitive) intermediate results are still protected across transactional parts. Through extensive experiments, we show that Mimosa effectively protects cryptographic keys against attacks that attempt to read sensitive data in memory, and introduces only a small performance overhead, even with concurrent cache-clogging workloads.

With the development of computing and communication technologies, extremely large amount of data has been collected, stored, utilized, and shared, while new security and privacy challenges arise. Existing platforms do not provide flexible and practical access control mechanisms for big data analytics applications. In this paper, we present GuardSpark++, a fine-grained access control mechanism for secure data sharing and analysis in Spark. In particular, we first propose a purpose-aware access control (PAAC) model, which introduces new concepts of data processing/operation purposes to conventional purpose-based access control. An automatic purpose analysis algorithm is developed to identify purposes from data analytics operations and queries, so that access control could be enforced accordingly. Moreover, we develop an access control mechanism in Spark Catalyst, which provides unified PAAC enforcement for heterogeneous data sources and upper-layer applications. We evaluate GuardSpark++ with five data sources and four structured data analytics engines in Spark. The experimental results show that GuardSpark++ provides effective access control functionalities with a very small performance overhead (average 3.97%).

Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, however, suffer from various kinds of threats, such as adversarial example attacks and fault injection attacks. While there are many defense methods proposed against maliciously crafted inputs, solutions against faults presented in the DNN system itself (e.g., parameters and calculations) are far less explored. In this paper, we develop a novel lightweight fault-tolerant solution for DNN-based systems, namely DeepDyve, which employs pre-trained neural networks that are far simpler and smaller than the original DNN for dynamic verification. The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much. We develop efficient and effective architecture and task exploration techniques to achieve optimized risk/overhead trade-off in DeepDyve. Experimental results show that DeepDyve can reduce 90% of the risks at around 10% overhead.

Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications. Recently, however, DNNs are shown to be vulnerable to adversarial example attacks with slight perturbations on the inputs. Existing defense mechanisms against such attacks try to improve the overall robustness of the system, but they do not differentiate different targeted attacks even though the corresponding impacts may vary significantly. To tackle this problem, we propose a novel configurable defense mechanism in this work, wherein we are able to flexibly tune the robustness of the system against different targeted attacks to satisfy application requirements. This is achieved by refining the DNN loss function with an attack sensitive matrix to represent the impacts of different targeted attacks. Experimental results on CIFAR-10 data set demonstrate the efficacy of the proposed solution.

Multimedia data needs huge storage space, and application of multimedia data needs powerful capability of computing. Cloud computing can help owner of multimedia data to deal with it. But, multimedia data on cloud may reveal privacy of data owner, such as sex, hobbies, address, looks, and so on. Data owner can encrypt multimedia data for confidentiality before uploading it to cloud. However, encrypted multimedia data makes its utilization difficult. In this paper, we first discover pre-existing schemes have problems of huge storage space, security and low efficiency due to their inefficient and insecure algorithms. Then, we provide an effective and practical privacy-preserving scale-invariant feature transform (SIFT) scheme for encrypted image. It uses leveled homomorphic encryption based on our new encoding schemes, our new homomorphic comparison, division and derivative encryption. Our new secure SIFT scheme can realize higher computing efficiency, greatly reduce communication costs and interactive times between user and server, and perform correct feature key point detection, accurate feature point description and image matching. We evaluate security and efficiency of our new secure SIFT scheme, and compare our new secure SIFT scheme with other schemes in detail. The result shows that it is closest to the original SIFT algorithm.

Tool wear prediction is of critical importance to maintain the desired part quality and improve productivity. Inspired by the successful application of deep learning in many condition monitoring tasks. In this article, a novel modeling framework is presented, which includes multiple stacked sparse autoencoders and a nonlinear regression function for tool wear prediction. Multiple stacked sparse autoencoders consists of two main structures. One model is designed with multidimensional stacked sparse autoencoders, which can learn more features from different feature domains in the raw vibration signal, and another single-dimensional stacked sparse autoencoders is used for feature fusion and deeper features learning. And a modified loss function is applied that improves the learning ability. In addition, due to the good properties of tool wear process in nonstationarity and complex nonlinear, a nonlinear regression function is utilized to enhance the progressive tool wear prediction tasks. A dataset from a real manufacturing process is used to evaluate the performance of the proposed modeling framework. Experimental results show that tool wear can be predicted accurately and stably by the proposed tool wear predictive model, which outperforms the already developed methods.

With the emergence of virtualization technologies, various services have been migrated to the cloud. Beyond the tenants' own security controls implemented in the virtual machine (VM), the binary integrity verification mechanism in the virtual machine manager (VMM) provides stronger protections against malware. Unfortunately, none of existing integrity verification mechanisms in the cloud provides complete transparency and fine-grained efficiency. Some schemes selectively check the integrity of sensitive binaries, but they require modifications to the VMs (e.g., integrating monitoring libraries) to trigger verification. Others, although need no modification to the VMs, have to enforce checking on all the binaries, because they cannot distinguish binary images for the sensitive processes from the binaries for insensitive ones, leading to significant performance overheads. In this paper, we present TF-BIV, a transparent and fine-grained binary integrity verification scheme, which does not require any modification or software/driver installation in the VM. TF-BIV identifies the sensitive processes at the creation, and checks the integrity of the binaries (including the guest OS kernel and the dependant binaries) related to these processes. The provided transparency and efficiency are achieved by leveraging existing hardware virtualization supports (i.e., Intel extended page table) and debugging features (i.e., monitor trap flag). We have implemented the TF-BIV prototype based on QEMU-KVM. To demonstrate the usability of TF-BIV, we adopted it for cloud-based cryptographic services, to achieve the strict invoking controls. In addition to the password-based authentication, TF-BIV further achieves process-level authorization to the invokers. Intensive evaluation shows that TF-BIV implements the designed binary integrity verification with only about 3.6% performance overhead.

Deep Neural Networks (DNNs) have attracted mainstream adoption in various application domains. Their reliability and security are therefore serious concerns in those safety-critical applications such as surveillance and medical systems. In this paper, we propose a novel dual modular redundancy framework for DNNs, namely D2NN, which is able to tradeoff the system robustness with overhead in a fine-grained manner. We evaluate D2NN framework with DNN models trained on MNIST and CIFAR10 datasets under fault injection attacks, and experimental results demonstrate the efficacy of our proposed solution.

Federated learning (FL) is promising in supporting collaborative learning applications that involve large datasets, massively distributed data owners and unreliable network connectivity. To protect data privacy, existing FL approaches adopt (k,n)-threshold secret sharing schemes, based on the semi-honest assumption for clients, to enable secure multiparty computation in local model update exchange which deals with random client dropouts at the cost of increasing data size. These approaches adopt the semi-honest assumption for clients, therefore they are vulnerable to malicious clients. In this work, we propose a blockchain-based privacy-preserving federated learning (BC-based PPFL) framework, which leverages the immutability and decentralized trust properties of blockchain to provide provenance of model updates. Our proof-of-concept implementation of BC-based PPFL demonstrates it is practical for secure aggregation of local model updates in the federated setting.

Machine learning systems based on deep neural networks (DNNs) produce state-of-the-art results in many applications. Considering the large amount of training data and know-how required to generate the network, it is more practical to use third-party DNN intellectual property (IP) cores for many designs. No doubt to say, it is essential for DNN IP vendors to provide test cases for functional validation without leaking their parameters to IP users. To satisfy this requirement, we propose to effectively generate test cases that activate parameters as many as possible and propagate their perturbations to outputs. Then the functionality of DNN IPs can be validated by only checking their outputs. However, it is difficult considering large numbers of parameters and highly non-linearity of DNNs. In this paper, we tackle this problem by judiciously selecting samples from the DNN training set and applying a gradient-based method to generate new test cases. Experimental results demonstrate the efficacy of our proposed solution.

Sensitive data in a process could be scattered over the memory of a computer system for a prolonged period of time. Unfortunately, DRAM chips were proven insecure in previous studies. The problem becomes worse in the mobile environment, in which users' smartphones are easily lost or stolen. The powered-on phones may contain sensitive data in the vulnerable DRAM chips. In this paper, we propose MemVault, a mechanism to protect sensitive data in Android devices against physical memory attacks. MemVault keeps track of the propagation of well-marked sensitive data sources, and selectively encrypts tainted sensitive memory contents in the DRAM chip. When a tainted object is accessed, MemVault redirects the access to the internal RAM (iRAM), where the cipher-text object is decrypted transparently. iRAM is a system-on-chip (SoC) component which is by nature immune to physical memory exploits. We have implemented a MemVault prototype system, and have evaluated it with extensive experiments. Our results validate that MemVault effectively eliminates the occurrences of clear-text sensitive objects in DRAM chips, and imposes acceptable overheads.

With the advancement of Internet of Things (IoT), a large number of electronic devices are connected to the Internet. These connected electronic devices acquire and transmit information, and respond to any received actions. In the medical ecosystem, hospitals can implement medical diagnosis (MD) with medical sensors, especially for remote auxiliary MD. But, in this context, patients' privacy (PP) is of paramount importance, and confidentiality of medical data is crucial. Therefore, the main challenge ahead is how to realize remote auxiliary MD while protecting confidentiality of the medical data and ensuring PP. In this article, based on somewhat homomorphic encryption (SHE) scheme addressed by Junfeng Fan and Frederik Vercauteren (FV), we provide the first instance of a new efficient SHE scheme for homomorphic evaluation over single instruction multiple data (SIMD). We also implement a new set of efficient SIMD homomorphic comparison and division schemes. Based on these findings, we implement efficient privacy preserving and SIMD homomorphic surf and multiretina-image matching schemes. Offered functionalities include SIMD homomorphic feature point detection, multiretina-image matching, and lesion detection for the encrypted retinal image of diabetic retinopathy. Finally, we provide a proof-of-concept application implementation toward remote auxiliary diagnosis systems for diabetes in order to showcase the core security and privacy pillars of our solution. In the meantime, our IoT system designed with lattice-based cryptography preserves data confidentiality under quantum computation and quantum computers.

Cache timing side channels allow a remote attacker to disclose the cryptographic keys, by repeatedly invoking the encryption/decryption functions and measuring the execution time. Warm and Delay are two algorithm-independent and implementation-transparent countermeasures against remote cache-based timing side channels for block ciphers. They destroy the relationship between the execution time and the cache misses/hits which are determined by the secret key, but bring remarkable performance overhead. In this paper, we investigate the performance of cryptographic functions protected by Warm and Delay, and attempt to find the best strategy to integrate these two countermeasures with the optimal performance while effectively eliminate remote cache timing side channels for block ciphers implementations with lookup tables. To the best of our knowledge, this work is the first to systematically analyze the performance of integrating Warm and Delay against cache side channels.We derive the optimal scheme to integrate Warm and Delay, and apply it to AES. It is proven that the integration scheme achieves the optimal performance with the least extra operations on commodity systems. Finally, we implement it on Linux with Intel CPUs. Experimental results confirm that, ( a) the execution time does not leak information on cache access, ( b) the scheme outperforms other integration strategies of Warm and Delay, and ( c) the implementation works without any privileged operations on the computer.

Abstract With the growing popularity of online social networks, a large amount of private or sensitive information has been posted online. In particular, studies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. As such, there exist great needs to be able to identify potentially-sensitive online contents, so that users could be alerted with such findings. In this paper, we propose a context-aware, text-based quantitative model for private information assessment, namelyPrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first solicit diverse opinions on the sensitiveness of private information from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to generate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs.

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input. Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes. In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world. In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example. Experimental results demonstrate the efficacy of the proposed technique.

Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis. No doubt to say, the data privacy of these deep learning systems is a serious concern. Different from previous research focusing on exploiting privacy leakage from deep learning models, in this paper, we present the first attack on the implementation of deep learning models. To be specific, we perform the attack on an FPGA-based convolutional neural network accelerator and we manage to recover the input image from the collected power traces without knowing the detailed parameters in the neural network. For the MNIST dataset, our power side-channel attack is able to achieve up to 89% recognition accuracy.

In Android malware detection, sensitive data-flows provide more accurate information on the application's behavior than regular features such as signatures and permissions. Currently, Android static taint analysis is widely adopted to identify sensitive data-flows because of its high code coverage and low false negative rate. However, existing static taint analysis tools cannot effectively analyze applications that adopt Android reflection mechanism. Reflection mechanism can block the control-flows and data-flows of the application. When constructing a call graph, the call information will point directly to the system's reflection processing method, rather than the actual method invoked by the application. This significantly affects the accurate representation of the application's behavior. To address this issue, this paper proposes a unified Android malware detection model based on reflective calls named URefFlow, in which the reflective call statement is replaced by the non-reflective call statement to make the reflective calls explicit by combining the parameters of the reflective calls into standard function calls. After extracting the complete sensitive data-flows with reflective calls from an application, we analyze the characteristics of these data-flows to determine whether the application is malicious. Evaluation results on thousands of applications show that URefFlow can achieve an impressive detection accuracy of 95.6% with a false positive rate of 0.8%. In addition, the proposed approach complements well with existing static stain analysis techniques.

With the dramatically increasing participation in online social networks (OSNs), huge amount of private information becomes available on such sites. It is critical to preserve users' privacy without preventing them from socialization and sharing. Unfortunately, existing solutions fall short meeting such requirements. We argue that the key component of OSN privacy protection is protecting (sensitive) content -- privacy as having the ability to control information dissemination. We follow the concepts of private information boundaries and restricted access and limited control to introduce a social circle model. We articulate the formal constructs of this model and the desired properties for privacy protection in the model. We show that the social circle model is efficient yet practical, which provides certain level of privacy protection capabilities to users, while still facilitates socialization. We then utilize this model to analyze the most popular social network platforms on the Internet (Facebook, Google+, WeChat, etc), and demonstrate the potential privacy vulnerabilities in some social networks. Finally, we discuss the implications of the analysis, and possible future directions.

Cryptosystems are essential for computer and communication security, e.g., RSA or ECDSA in PGP Email clients and AES in full disk encryption. In practice, the cryptographic keys are loaded and stored in RAM as plain-text, and therefore vulnerable to cold-boot attacks exploiting the remanence effect of RAM chips to directly read memory data. To tackle this problem, we propose Copker, a cryptographic engine that implements asymmetric cryptosystems entirely within the CPU, without storing any plain-text sensitive data in RAM. Copker supports the popular asymmetric cryptosystems (i.e., RSA and ECDSA), and deterministic random bit generators (DRBGs) used in ECDSA signing. In its active mode, Copker stores kilobytes of sensitive data, including the private key, the DRBG seed and intermediate states, only in on-chip CPU caches (and registers). Decryption/signing operations are performed without storing any sensitive information in RAM. In the suspend mode, Copker stores symmetrically-encrypted private keys and DRBG seeds in memory, while employs existing solutions to keep the key-encryption key securely in CPU registers. Hence, Copker releases the system resources in the suspend mode. We implement Copker with the support of multiple private keys. With security analyses and intensive experiments, we demonstrate that Copker provides cryptographic services that are secure against cold-boot attacks and introduce reasonable overhead.

The increasing growth of cybercrimes targeting mobile devices urges an efficient malware analysis platform. With the emergence of evasive malware, which is capable of detecting that it is being analyzed in virtualized environments, bare-metal analysis has become the definitive resort. Existing works mainly focus on extracting the malicious behaviors exposed during bare-metal analysis. However, after malware analysis, it is equally important to quickly restore the system to a clean state to examine the next sample. Unfortunately, state-of-the-art solutions on mobile platforms can only restore the disk, and require a time-consuming system reboot. In addition, all of the existing works require some in-guest components to assist the restoration. Therefore, a kernel-level malware is still able to detect the presence of the in-guest components.

Deep neural network (DNN), being able to effectively learn from a training set and provide highly accurate classification results, has become the de-facto technique used in many mission-critical systems. The security of DNN itself is therefore of great concern. In this paper, we investigate the impact of fault injection attacks on DNN, wherein attackers try to misclassify a specified input pattern into an adversarial class by modifying the parameters used in DNN via fault injection. We propose two kinds of fault injection attacks to achieve this objective. Without considering stealthiness of the attack, single bias attack (SBA) only requires to modify one parameter in DNN for misclassification, based on the observation that the outputs of DNN may linearly depend on some parameters. Gradient descent attack (GDA) takes stealthiness into consideration. By controlling the amount of modification to DNN parameters, GDA is able to minimize the fault injection impact on input patterns other than the specified one. Experimental results demonstrate the effectiveness and efficiency of the proposed attacks.

Object representation is a major component in object tracking, however, most conventional patch-based methods just simply decompose the object into patches with grid or stochastic rectangles. This kind of decomposition ignores the intrinsic structure of object, leading to low discriminative power and weak representation effectiveness when similar objects appear or under background clutters. In this paper, we propose an effective object descriptor based on a hierarchical representation with superpixels for visual tracking, called Structural Superpixel Descriptor (SSD). The proposed SSD not only exploits the superpixels to capture the structural information of object, but also preserves the spatial layout structure among the superpixels inside each target candidate. Moreover, we propose an adaptive patch weighting method based on spatial constraint to alleviate various adverse impacts of background information, making the tracker more robust against background noises. We show that the proposed SSD makes full use of the intrinsic structure inside target candidates. Extensive experiments conducted on various challenging sequences demonstrate that the proposed tracker performs well against state-of-the-art algorithms.

With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. In this paper, we study and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: 1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; 2) from the CPS components perspective, we focus on cyber, physical, and cyberphysical components; and 3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS, and smart cars). The model can be both abstract to show general interactions of components in a CPS application, and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection. With this intensive literature review, we attempt to summarize the state-of-the-art on CPS security, provide researchers with a comprehensive list of references, and also encourage the audience to further explore this emerging field.

Automatic social circle detection in ego-networks is a fundamentally important task for social network analysis. So far, most studies focused on how to detect overlapping circles or how to detect based on both network structure and node profiles. This paper asks an orthogonal research question: how to detect circles by leveraging multiple views of the network structure? As a first step, we crawl ego networks from Twitter and model them by six views, including user relationships, user interactions, and user content. We then apply both standard and our modified multi-view spectral clustering techniques to detect circles on these ego-networks. By extensive automatic and manual evaluations, we deliver two major findings: first, multi-view clustering techniques detect better circles than single-view clustering methods; second, our modified clustering technique which presumes sparse networks are incomplete detects better circles than the standard clustering technique which ignores such potential incompleteness. In particular, the second finding makes us conjecture a direct application of standard clustering on potentially incomplete networks may yield biased results. We lightly investigate this issue by deriving a bias upper bound that integrates theories of spectral clustering and matrix perturbation, and discussing how the bound may be affected by several network characteristics.

Most existing discriminative tracking methods model a target object as a whole and train a tracker based on holistic templates, which cannot effectively deal with partial occlusions. Instead, in this paper, by treating the target as a collection of local patches, we propose a novel tracking approach based on boosted local classifiers. Initially, a set of local patches are sampled to train a set of local classifiers, and the weight of each classifier is given based on the estimated error. In addition, the positive examples and negative examples are sampled for model update with two constraints during the tracking process, which helps obtain more negatives for updating the appearance model and improve the updating efficiency. With updating the weights of local classifiers based on the temporal stability, the tracker can effectively handle partial occlusions. Extensive experiments on various challenging image sequences demonstrate the superiority to several state-of-the-art methods.

The Treatise on Invertebrate Paleontology is the most reliable information source of invertebrate paleontology research. Based on this Treatise, an Invertebrate Paleontology Knowledgebase (IPKB) has been built as a digital library to provide these data through a web interface. However, the search functions provided by the old IPKB system are only based on textual information, while some more important information, such as textual ontology and fossil images, are not considered at all. In order to overcome this limitation, and provide more reliable and flexible search options, we develop a new hybrid search function for the current IPKB system. In particular, we propose an approach to extract textual ontology information for each genus, as well as build a fossil image dataset where each image is tagged with its genus name. Based on the data from both sources, a hybrid search system is developed by integrating both textual and visual features, and thus, more search options are available to users and the searching results are significantly improved.

Hardware primitives provide significant promises to support cryptographic primitives and security mechanisms against various forms of compromises. In this work, we study the intrinsic hardware characteristics of modern graphics processing units (GPUs) due to random manufacturing variations, and exploits the inherent randomness to generate device-specific signatures. In particular, we present a novel GPU-based hardware fingerprint scheme to generate a unique, stable, physically unclonable, unpredictable, and random bit string from the inherent hardware features of a general purpose GPU (GPGPU). The generated fingerprint can be used to implement a physically unclonable function (PUF), and thus to create a trusted computing environment with GPUs as the trust anchor.

Cryptography plays an important role in computer and communication security. In practical implementations of cryptosystems, the cryptographic keys are usually loaded into the memory as plaintext, and then used in the cryptographic algorithms. Therefore, the private keys are subject to memory disclosure attacks that read unauthorized data from RAM. Such attacks could be performed through software methods (e.g., Open SSL Heart bleed) even when the integrity of the victim system's executable binaries is maintained. They could also be performed through physical methods (e.g., Cold-boot attacks on RAM chips) even when the system is free of software vulnerabilities. In this paper, we propose Mimosa that protects RSA private keys against the above software-based and physical memory attacks. When the Mimosa service is in idle, private keys are encrypted and reside in memory as cipher text. During the cryptographic computing, Mimosa uses hardware transactional memory (HTM) to ensure that (a) whenever a malicious process other than Mimosa attempts to read the plaintext private key, the transaction aborts and all sensitive data are automatically cleared with hardware mechanisms, due to the strong atomicity guarantee of HTM, and (b) all sensitive data, including private keys and intermediate states, appear as plaintext only within CPU-bound caches, and are never loaded to RAM chips. To the best of our knowledge, Mimosa is the first solution to use transactional memory to protect sensitive data against memory disclosure attacks. We have implemented Mimosa on a commodity machine with Intel Core i7 Haswell CPUs. Through extensive experiments, we show that Mimosa effectively protects cryptographic keys against various attacks that attempt to read sensitive data from memory, and it only introduces a small performance overhead.

With the increasing usage of XML on information sharing over the Internet, a mechanism for defining and enforcing XML access control is demanded, such that only authorized entities can access the sets of XML data that they are allowed to. The research interests in these areas have grown significantly in recent years. Various access control enforcement solutions have been proposed, each with its inherent advantages and disadvantages. Yet, there is still no solution that can provide superior performance in all situations. In this paper, we present HyXAC, a hybrid approach to enforce XML access control. HyXAC integrates the two most popular categories of XML access control enforcement mechanisms, and earns the benefits from both. In particular, HyXAC first preprocesses user queries by rewriting queries and removing parts violating access control rules, and evaluates the re-written queries using subviews, if they are available. In HyXAC, views are not defined on a per-role basis. Instead, a sub-view is defined for each access control rule, and roles sharing identical rules will share sub-views. Moreover, HyXAC dynamically allocates memory and secondary storage resources to materialize and cache sub-views to improve query performance. We have conducted extensive experiments, and the results show that HyXAC improves query processing efficiency while optimizes the use of system resources.

In conventional database access control models, access control policies are explicitly specified for each role against each data object. In large-scale content-centric data sharing, it might be difficult to explicitly identify accessible records for each role/user, especially when the semantic content of data is expected to play a role in access decisions. As a result, users are often over-privileged, and ex post facto auditing is enforced to detect misuse of the privileges. Unfortunately, it is usually difficult to reverse the damage, as (large amount of) data has been disclosed already. In this paper, we introduce Content-Based Access Control (CBAC), an innovative access control model for content-centric information sharing. CBAC is expected to be deployed on top of Role-Based Access Control (RBAC) or Multi-level Security (MLS), in the application scenarios where RBAC and MLS will give excessive access rights. As a complement to conventional access control models, the CBAC model makes access control decisions based on the content similarity. In CBAC, each user is allowed by an MLS or RBAC rule to access a large set of data objects, while the CBAC rule imposes an additional layer of restrictions that the user could only access “a subset” of the designated records. The boundary of the subset is dynamically determined by the textual content of data objects. We then present an enforcement mechanism for CBAC that exploits Oracle's Virtual Private Database (VPD). To further improve the performance of the proposed approach, we introduce a content-based blocking mechanism to improve the efficiency of CBAC enforcement. We also develop a content annotation mechanism for more accurate textual content matching for short text snippets. Experimental results show that CBAC makes reasonable access control decisions with a small overhead.

With the development of information technology, online social networks grow dramatically. They now play a significant role in people's social life, especially for the younger generation. While huge amount of information is available in online social networks, privacy concerns arise. Among various privacy protection proposals, the notions of privacy as control and information boundary have been introduced. Commercial social networking sites have adopted the concept to implement mechanisms such as Google circles and Facebook custom lists. However, the functions are not widely accepted by the users, partly because it is tedious and labor-intensive to manually assign friends into circles.

We propose a novel local-based saliency measure (LBSM) method for object tracking problem. In LBSM method, salient patches are defined as the patches having great local changes. Then we apply the saliency information derived from LBSM to appearance model by giving weights to patches according to their saliency levels. The patches with higher saliency levels are given larger weights. As a result, the appearance model is improved owing to the use of saliency information. Extensive experiments conducted on various challenging sequences demonstrate the effectiveness of LB-SM in tracking procedure, and our saliency-based tracker performs well against state-of-the-art algorithms.

Currently, a large amount of data is produced in healthcare informatics due to the growth of web technologies like social networks, wikis, blogs and RSS feeds. However, not all health information provided online is trustworthy. Even though many experts are involved in publishing trusted information, it is difficult for the general population to determine the credibility of the information. Therefore, a reliable mechanism to automatically determine the trustworthiness of online healthcare information is highly desired. In this paper, we propose two novel approaches based on Topic Modeling and Hidden Markov Models (HMMs), that can be applied over a large volume of online healthcare data to assess its trustworthiness. Traditional Topic Modeling is solely based on the “bag-of-words” model, however, we also consider the semantics of the content to identify the underlying topics in a sentence. For the HMM approach, we built our trustworthy and suspicious models after analyzing the characteristics of sentences from such websites. Both methods perform well to assess the trustworthiness, however HMM is less sophisticated to capture the semantics of sentences. We evaluated our method on randomly chosen real dataset and are able to achieve about 90% accuracy in identifying the trustworthiness of the content.

Conventional database access control models have difficulties in dealing with big data, especially for the features of volume, variety and velocity. To address the problem, we introduce the Content-based Access Control (CBAC) model for content-centric information sharing. As a complement to conventional models, CBAC makes access control decisions based on the content similarity between user credentials and data content dynamically. We present an enforcement mechanism for CBAC exploiting Oracle's Virtual Private Database (VPD). Experimental results show that CBAC makes reasonable access control decision with a small overhead.

While XML has been widely adopted for information sharing over the Internet, the need for efficient XML access control naturally arise. Various XML access control enforcement mechanisms have been proposed in the research community, such as view-based approaches and pre-processing approaches. Each category of solutions has its inherent advantages and disadvantages. For instance, view based approach provides high performance in query evaluation, but suffers from the view maintenance issues. To remedy the problems, we propose a hybrid approach, namely HyXAC: Hybrid XML Access Control. HyXAC provides efficient access control and query processing by maximizing the utilization of available (but constrained) resources. HyXAC first uses the pre-processing approach as a baseline to process queries and define sub-views. In HyXAC, views are not defined in a per-role basis, instead, a sub-view is defined for each access control rule, and roles with identical rules would share the sub-view. Moreover, HyXAC dynamically allocates the available resources (memory and secondary storage) to materialize and cache sub-views to improve query performance. With intensive experiments, we have shown that HyXAC optimizes the usage of system resource, and improves the performance of query processing.

Today's organizations raise an increasing need for information sharing via on-demand access. Information brokering systems (IBSs) have been proposed to connect large-scale loosely federated data sources via a brokering overlay, in which the brokers make routing decisions to direct client queries to the requested data servers. Many existing IBSs assume that brokers are trusted and thus only adopt server-side access control for data confidentiality. However, privacy of data location and data consumer can still be inferred from metadata (such as query and access control rules) exchanged within the IBS, but little attention has been put on its protection. In this paper, we propose a novel approach to preserve privacy of multiple stakeholders involved in the information brokering process. We are among the first to formally define two privacy attacks, namely attribute-correlation attack and inference attack, and propose two countermeasure schemes automaton segmentation and query segment encryption to securely share the routing decision-making responsibility among a selected set of brokering servers. With comprehensive security analysis and experimental results, we show that our approach seamlessly integrates security enforcement with query routing to provide system-wide security with insignificant overhead.

With the development of Internet and Web 2.0, large-volume multimedia contents have been made available online. It is highly desired to provide easy accessibility to such contents, i.e., efficient and precise retrieval of images that satisfies users' needs. Toward this goal, content-based image retrieval (CBIR) has been intensively studied in the research community, while text-based search is better adopted in the industry. Both approaches have inherent disadvantages and limitations. Therefore, unlike the great success of text search, web image search engines are still premature. In this paper, we present iLike, a vertical image search engine that integrates both textual and visual features to improve retrieval performance. We bridge the semantic gap by capturing the meaning of each text term in the visual feature space, and reweight visual features according to their significance to the query terms. We also bridge the user intention gap because we are able to infer the "visual meanings" behind the textual queries. Last but not least, we provide a visual thesaurus, which is generated from the statistical similarity between the visual space representation of textual terms. Experimental results show that our approach improves both precision and recall, compared with content-based or text-based image retrieval techniques. More importantly, search results from iLike is more consistent with users' perception of the query terms.

How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.

Security protection is an integral component for smart homes; however, smart appliances security has received little attention in the research community. Household appliances become very vulnerable if we introduce smart functions without proper security protection. In particular, smart access functions enable users to operate devices remotely. Meanwhile, smart devices are are also designed to support residential demand response, i.e. postpone non-urgent tasks to non-peak hours. However, remote adversaries could utilize such functions to manipulate smart appliances' operations without physically touching them. Such interferences, if not properly handled, could damage the smart devices, disturb owners' life or even harm the households' physical security.

With the extreme popularity of Web and online social networks, a large amount of personal information has been made available over the Internet. On the other hand, advances in information retrieval, data mining and knowledge discovery technologies have enabled users to efficiently satisfy their information needs over the Internet or from large-scale data sets. However, such technologies also help the adversaries such as web stalkers to discover private information about their victims from mass data.

Location context in social media plays an important role in many applications. In addition to explicit location sharing via popular "check in" service, user-posted content could also implicitly reveals users' location context. Identifying such a location context based on content is an interesting problem because it is not only important in inferring social ties between people, but also vital for applications such as user profiling and targeted advertising. In this paper, we study the problem of location type classification using tweet content. We extend probabilistic text classification models to incorporate temporal features and user history information in terms of probabilistic priors. Experimental results show that our extensions can boost classification accuracy effectively.

Distributed storage systems are expected to provide correct services in the presence of Byzantine failures, which do not have any assumptions about the behavior of faulty servers and clients. In designing such systems, we often encounter the paradox of fault tolerance vs. performance (or efficiency), because better fault tolerance usually requires a tradeoff of system performance. In this paper, we present GRADE, a Byzantine-fault-tolerant (BFT) distributed storage system that enables graceful degradation. Two Byzantine quorum systems (BQSs) are supported on each GRADE server: a masking BQS storing generic data and a dissemination BQS storing self-verifying ones. Based on the system status and the environment, servers dynamically and seamlessly switch between two BQSs, without converting the stored data. Therefore, GRADE provides high performance in a normal running-state, and degrades performance to maintain high fault tolerance in emergency situations. The computation and communication costs of the running-state switch are very low, and the switch is completely transparent to clients. Our performance analysis and experimental results demonstrate that GRADE provides a balance between performance and fault tolerance.

Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a "microeconomics" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r.t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.

With rising concerns on user privacy over the Internet, anonymous communication systems that hide the identity of a participant from its partner or third parties are highly desired. Existing approaches either rely on a relative small set of pre-selected relay servers to redirect the messages, or use structured peer-to-peer systems to multicast messages among a set of relay groups. The pre-selection approaches provide good anonymity, but suffer from node failures and scalability problem. The peer-to-peer approaches are subject to node churns and high maintenance overhead, which are the intrinsic problems of P2P systems. In this paper, we present CAT, a node-failure-resilient anonymous communication protocol. In this protocol, relay servers are randomly assigned to relay groups. The initiator of a connection selects a set of relay groups instead of relay servers to set up anonymous paths. A valid path consists of relay servers, one from each selected relay group. The initiator explores valid anonymous paths via a probing process. Since the relative positions of relay servers in the path are commutative, there exist multiple anonymous yet commutative paths, which form an anonymous tunnel. When a connection encounters a node failure, it quickly switches to a nearest backup path in the tunnel through "path hopping", without tampering the initiator or renegotiating the keys. Hence, the protocol is resilient to node failures. We also show that the protocol provides good anonymity even when facing types of active and passive attacks. Finally, the operating cost of CAT is analyzed and shown to be similar to other node-based anonymous communication protocols.

Content-based image search on the Internet is a challenging problem, mostly due to the semantic gap between low-level visual features and high-level content, as well as the excessive computation brought by huge amount of images and high dimensional features. In this paper, we present iLike, a new approach to truly combine textual features from web pages, and visual features from image content for better image search in a vertical search engine. We tackle the first problem by trying to capture the meaning of each text term in the visual feature space, and re-weight visual features according to their significance to the query content. Our experimental results in product search for apparels and accessories demonstrate the effectiveness of iLike and its capability of bridging semantic gaps between visual features and abstract concepts.

As online social networks get more popular, it becomes increasingly critical to preserve user privacy in such networks. In this paper, we propose our preliminary results on defining and tackling information aggregation attacks over online social networks. We first introduce three major threats towards private information in online social networks. We conceptually model private information into multilevel and discretionary models. Then, we articulate information aggregation attacks under discretionary model. Finally, we present our preliminary design of "privacy monitor," a framework that allows users to define their own privacy scheme, and track their actual privacy disclosure to check for any unwanted leakage.

A Distributed Information Brokering System (DIBS) is a peer-to-peer overlay network that comprises diverse data servers and brokering components helping client queries locate the data server(s). Many existing information brokering systems adopt server side access control deployment and honest assumptions on brokers. However, little attention has been drawn on privacy of data and metadata stored and exchanged within DIBS. In this paper, we address privacy-preserving information sharing via on-demand information access. We propose a flexible and scalable system using a broker-coordinator overlay network. Through an innovative automaton segmentation scheme, distributed access control enforcement, and query segment encryption, our system integrates security enforcement and query forwarding while preserving system-wide privacy. We present the automaton segmentation approach, analyze privacy preservation in details, and finally examine the end-to-end performance and scalability through experiments and analysis.

At present, most of the state-of-the-art solutions for XML access controls are either (1) document-level access control techniques that are too limited to support fine-grained security enforcement; (2) view-based approaches that are often expensive to create and maintain; or (3) impractical proposals that require substantial security-related support from underlying XML databases. In this paper, we take a different approach that assumes no security support from underlying XML databases and examine three alternative fine-grained XML access control solutions, namely primitive, pre-processing and post-processing approaches. In particular, we advocate a pre-processing method called QFilter that uses Non-deterministic Finite Automata (NFA) to rewrite user's query such that any parts violating access control rules are pruned. We show the construction and execution of a QFilter and demonstrate its superiority to other competing methods.

The synchronized multimedia integration language (SML/spl trade/) fulfills the needs of integration, synchronization, and efficient online delivery of different media types such as text, music, speech, image, and video. In this paper, we represent these multimedia elements in a synchronized manner under a unified feature space. An efficient SMIL retrieval scheme based on textual feature and content feature is proposed. Pilot experiments on our SMIL database show that the proposed method can work well on SMIL retrieval.

Video caption detection and extraction is an important step for information retrieval in video databases. In this paper, we extract text information in video by fully utilizing the temporal information contained in the video. First we create a binary abstract sequence from a video segment. By analyzing the statistical pixel changes in the sequence, we can effectively locate the (dis)appealing frames of captions. Finally we extract the captions to create a summary of the video segment.

A new caption text extraction algorithm that takes full advantage of the temporal information in a video sequence is developed. By detecting the (dis)appearance of caption text in a video stream, we first identify the video segment that contains the same caption text. Then using the gray-level vector traced across the segment as the feature vector for a pixel point, we can clearly separate a caption pixel from a background pixel for the entire segment.