Wireless Rechargeable Sensor Networks (WRSNs) promise to address the limited energy resource issue for sensor nodes through wireless power transfer technology. However, WRSNs are vulnerable to various security threats, such as compromised node attack and malicious mobile charger (MC) attack, which can disrupt the charging process and degrade charging efficiency. In this work, we investigate the eneRgy conversionEfficiency maximization problem unDer chargIng attackS(REDIS). We propose a blockchain-based framework that employs a lightweight multi-layer storage approach tailored for resource-constrained sensor nodes and features consensus algorithms that validate charging transactions. Furthermore, we introduce a validation node selection strategy that integrates consensus execution with charging scheduling, reducing energy consumption, and improving energy efficiency. Extensive simulations and experiments validate the effectiveness of our framework, improving energy efficiency by 30% and as much as 5 times in networks without attacks and those under full attacks, respectively.

Gradient leakage attacks pose a significant privacy threat in distributed learning-as-a-service APIs. Existing literature on gradient leakage defense relies on gradient perturbation for preventing privacy leakage. However, determining where and how much to perturb the gradient offers different capabilities for preventing gradient leakage. This paper presents GradCloak, a principled approach to guiding gradient perturbation with theoretical robustness bounds in federated learning as a service, aiming to find the minimum required noise for simultaneously achieving privacy protection, competitive accuracy, and preventing gradient leakage attacks. The paper is organized into three major components.First, we formulate the gradient leakage threats and their adverse effect. We categorize the attack into two broad types: leakage during local training and leakage before global aggregation.Second, we investigate different gradient perturbation approaches. We analyze and compare these gradient perturbation methods, which are performed at the federated server, with those performed at the participating client(s).Third, we introduce three robustness properties of robust perturbation against gradient leakage threats, formulated bythe anonymization boundfor training data robustness,the perturbation boundfor gradient robustness, andthe distribution robustness boundfor perturbed gradients. We conduct extensive evaluations on eight benchmark datasets to demonstrate that specific settings of gradient perturbation exist that best balance privacy, accuracy, and leakage prevention. Code is available athttps://github.com/git-disl/GradCloak.

Adversarial purification has emerged as a critical defense mechanism against various adversarial attacks on deep neural networks, however, such category of diffusion-based purification in the pixel space confronts two challenges while applying into today’s large vision transformers: i) the distributional divergence between adversarial and clean examples in high-dimensional image manifold; and ii) the prohibitive computational cost while processing high-resolution images. To address the predicament, we in this paper focus on the transformer-based visual encoders commonly employed in large Vision-Language Models (VLMs), and propose a novel Latent Diffusion-based Purification (LDP) mechanism through leveraging the latent space to pave the gap between adversarial distribution and clean distribution. Resorting to projecting adversarial input into a low-dimensional latent representation, our proposed LDP not only suppresses the off-manifold perturbations to achieve accelerated denoising, but also preserves critical semantic features by aligning input with the priori high-quality visual representations. Multi-facet experiments over both proactive robustness enhancement and post-attack purification demonstrate that our LDP has a superior performance in terms of effectiveness and efficiency.

In this demo, we describe the process of integrating typical Web2-based user authentication mechanisms into the ERC-4337 account abstraction (AA) and use the passkey-based authentication as an example to illustrate how to manage a smart contract wallet (SCW) using a passkey

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on code search. However, existing code search methods still suffer from two performance constraints: inadequate semantic representation and the semantic gap between natural language (NL) and programming language (PL). In this paper, we propose CPLCS, a contrastive prompt learning-based code search method based on the cross-modal interaction mechanism. CPLCS comprises: (1) PL-NL contrastive learning, which learns the semantic matching relationship between PL and NL representations; (2) a prompt learning design for a dual-encoder structure that can alleviate the problem of inadequate semantic representation; (3) a cross-modal interaction mechanism to enhance the fine-grained mapping between NL and PL. We conduct extensive experiments to evaluate the effectiveness of our approach on a real-world dataset across six programming languages. The experiment results demonstrate the efficacy of our approach in improving semantic representation quality and mapping ability between PL and NL.

In the field of human-computer interaction (HCI), handwriting trajectory tracking and recognition have attracted significant attention due to their wide range of applications. However, many existing approaches rely on handheld devices and are highly susceptible to factors such as environmental conditions, location, and writing style. To overcome these limitations, we propose AirWrite, a novel contactless aerial system for handwriting trajectory tracking and recognition using mmWave technology. We introduce a signal clipping method based on the Doppler effect caused by user actions to accurately remove non-handwriting signals in the time domain. Additionally, we analyze power variations within the signal frequency interval to determine the handwriting frequency and employ a band-pass filter to eliminate dynamic environmental noise effectively. Through extensive experiments, we demonstrate that AirWrite can precisely track handwriting trajectories in noisy environments regardless of distance, angle, handwriting speed, character size, or in the presence of obstacles. Furthermore, we present an effective handwritten character recognition method for AirWrite that recognizes alphabets, numbers, and words. AirWrite can achieve an average accuracy of over 96% with only a 34 KB small dataset within 0.15 s for recognition.

In this demo, we illustrate the process of creating a machine economy utilizing W3bstream - an emerging open-source framework designed for connecting smart devices to smart contracts. We will emphasize the flexibility and user-friendliness of W3bstream in empowering decentralized IoT applications.

Trusted execution environment (TEE) technology has many uses, such as protecting data in the cloud and improving security for industrial IoT. However, there are technical challenges that limit its widespread adoption. These challenges include the fact that different TEE vendors have incompatible solutions, and devices equipped with the same TEE technology may belong to different owners, making it difficult to establish trust between them. To address these challenges and fully utilize TEE technology, a decentralized coordination mechanism called DHTee is proposed. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially attestation service. Devices equipped with TEE can interact securely with the blockchain to determine whether potential collaborating devices meet the requirements. DHTee is also flexible and can support new TEE schemes without affecting existing TEEs.

Trajectory similarity computation as the fundamental problem for various downstream analytic tasks, such as trajectory classification and clustering, has been extensively studied in recent years. However, how to infer an accurate and robust similarity over two trajectories is difficult due to the some trajectory characteristics in practice, e.g. non-uniform sampling rate, nonmalignant fluctuation, and noise points, etc. To circumvent such challenges, we in this paper introduce the adversarial training idea into the trajectory representation learning for the first time to enhance the robustness and accuracy. Specifically, our proposed method AdvTraj2Vec has two novelties: i) it perturbs the weight parameters of embedding layers to learn a robust model to infer an accurate pairwise similarity over each two trajectories; and ii) it employs the GAN momentum to harness the perturbation extent to which an appropriate trajectory representation can be learned for the similarity computation. Extensive experiments using two real-world trajectory datasets Porto and Beijing validate our proposed AdvTraj2Vec on the robustness and accuracy aspects. The multi-facet results show that our AdvTraj2Vec significantly outperforms the stat-of-the-art methods in terms of different distortions, such as trajectory-point addition, deletion, disturbance, and outlier injection.

With the development of deep learning, deep convolution neural networks for medical image segmentation tasks have become more and more complex in pursuit of higher accuracy. In most scenarios, medical image segmentation pursues accuracy rather than speed, However, real-time performance is crucial in some scenarios, such as surgical navigation and diagnosis of acute stroke. So design of high-precision, lightweight and real-time medical image segmentation network has become an urgent need. To this end, a novel lightweight dual-domain network (LDD-Net) has been proposed in this paper. LDD-Net is comprised of two branches, learning respectively from the frequency domain and the spatial domain. In the frequency domain branch, the image spatial resolution is compressed via discrete cosine transform to have a large receptive field, so that better semantic context features can be learned. In the spatial domain branch, high-resolution feature representations with more details are learned. Finally, the learned features of these two branches are fused to yield high accuracy with low computational cost. The proposed method has been validated on two medical image segmentation datasets to yield the state-of-the-art performances with greatly reduced inference time and parameters of the learned models.

To circumvent the grave problem of present adversarial training methods, i.e. distortion of classification surface, we in this paper propose a generated Triplet-based adversarial training method-GTAT, in which a Generator generates a semi-hard Triplet by design, rather than directly invoking the existing clean examples and adversarial examples. Through this kind of generated semi-hard Triplet constraint, GTAT can reshape the classification boundaries appropriately across various classes, arising from two-facet synergies: i) pull the intra-class examples together with tight distances; and ii) push away the inter-class examples with broad distances. This synergy will simplify and broaden the classification surfaces across different classes. Extensive experiments on the popular MNIST and CIFAR-10 datasets show that our proposed GTAT significantly outperforms other state-of-the-art adversarial training methods. We believe GTAT opens a door for the adversarial training from a new horizon of rationally generating semi-hard Triplet-satisfied adversarial training (retraining) examples, instead of straightly performing retraining on the generated adversarial examples and existing clean examples, or on the generated adversarial examples only.

With the purpose of saving the developing time of software engineers and promoting the work efficiency of programs, the research on automated source-code summarization (SCS) has become necessary in recent years, i.e. generating language descriptions for source code. To date, there exist two categories of SCS methods: information retrieval (IR)-based SCS and neural-based SCS. The latter is the mainstream method at present, however, this line of work suffers from the drawback of incapability to generate low-frequency words, which potentially degrades the performance. To tackle this predicament, we in this paper propose an IR-enhanced neural SCS method RetCom to improve the prediction of low-frequency words through leveraging both structural-level and semantic-level code retrievals. Furthermore, we figure out a token-level context-dependent mixture network to fuse different information sources, i.e. original code, structurally most similar code, and semantically most similar code. Finally, extensive experiments are performed to validate our proposed RetCom using two real-world datasets. Compared to several baseline methods, the experimental results show that our method does validly capture more low-frequency words to conduct a superior performance.

In this paper, we propose a new task namely trajectory cross-modal retrieval which achieves the cross-modal search between coordinate trajectories and images containing trajectories. Nevertheless, trajectory cross-modal retrieval is rather challenging in learning the representations of each modality and reduce the cross-domain discrepancy caused by the inconsistent data distribution at the same time. we proposes a cross-modal retrieval model TrajCross based on multi-level representation for trajectory cross-modal retrieval. Specifically, TrajCross extracts the location features and the shape information respectively for the represention of multi-modal data. we adopt a contrastive learning method to achieve semantic preservation among similar multi-modal data. Extensive experiments show that TrajCross significantly outperforms state-of-the-art cross-modal retrieval methods.

User identity linkage (UIL) task aims to infer the identical users between different social networks/platforms. Existing models leverage the labeled inter-linkages or high-quality user attributes to make predictions. Nevertheless, it is often difficult or even impossible to obtain such information in real-world applications. To this end, we in this paper focus on studying an Anonymized User Identity Linkage (AUIL) problem wherein neither labeled anchor users nor attributes are available. To handle such a practical and challenging task, we propose a novel and concise unsupervised embedding method, VCNE, by utilizing the network structural information. Concretely, considering the inherent properties of structural diversity in the AUIL problem, we introduce a variational cross-network embedding learning framework to jointly study the Gaussian embeddings instead of the existing deterministic embedding from the angle of vector space. The multi-facet experiments on both real-world and synthetic datasets demonstrate that VCNE not only outperforms all baselines to a large extent but also be more robust to the different-level diversities and sparsities of the networks.

Social media has became a critical manner for people to acquire information in daily life. Despite the great convenience, fake news can be widely spread through social networks, causing various adverse effects on people's lives. Detecting these fake news or misinformations has proved to be a critical task and draws attentions from both governments and individuals. Recently, many methods have been proposed to solve this problem, but most of them rely on the body content of the news, ignoring the social context information such as the comments. We argue that the comments of a specific news contain common judgements of the whole society and could be extremely useful for detecting fake news. In this paper, we propose a new method TRANSFAKE which jointly models the body content and comments of news systemically, and detects fake news with multi-task learning framework. TRANSFAKE model is a Transformer-based model. It takes different modalities as input and employs multiple tasks, i.e. rumor score prediction and event classification, as intermediate tasks for extracting useful hidden relationships across various modalities. These intermediate tasks promote each other and encourage TRANSFAKE making the right decision. Extensive experiments on two standard real-life datasets demonstrate that TRANSFAKE outperforms state-of-the-art methods. It improves the detection accuracy by margins as large as ~12.6% and F1 scores as large as ~15%.

Recently, a new methodology using generative adversarial network (GAN) has been proposed to produce adversarial examples, which breaks the limitations of the previous methods dependent on different norm-levels. It can efficiently generate perturbations for any instance once the generator is trained, arising from the learning to approximate the distribution of real instances. However, there are still two shortcomings for this category of GAN-based method: i) the predicted label in attacking stage totally depend on a fixed or randomly-chosen label in training stage, which cannot tackle the elasticity problem on how to elastically produce adversarial example with any arbitrarily-assigned label in targeted attack scene when the generator has finished training; and ii) it only considering the produced adversarial example is as close as the real instances, which cannot guarantee the generated adversarial example is visually indistinguishable from its corresponding original instance perceptually. The aboved two disadvantages make this kind of method lack of flexibility and covertness. To circumvent these two predicaments, we in this paper propose a simple and easy-to-use adversarial example generating framework AdvCGAN through training a conditional generative adversarial network under the co-consideration on the similarities in data distributions and the image labels between the adversarial examples and the original instances to be imperceptible to humans. Concretely, our proposed AdvCGAN trains the conditional GAN with both image data and label (normal and attack) information, by which the generator can utilizing the guidance of label information to appropriately produce the adversarial example with any specific label in attacking stage. Extensive experiments using the commonly used MNIST and CIFAR-10 datasets show that our proposed AdvCGAN significantly outperforms other methods in terms of multi-facet evaluation. The results exhibit that our AdvCGAN can elastically produce more realistic adversarial examples with any arbitrarily-assigned attack label and achieve higher attack accuracy, especially in targeted attack.

Heterogeneous information networks (HINs) have been successfully applied into several fields to accomplish complex data analytics, such as bibliography, bioinformatics, NLP, etc. In the meantime, network embedding at present has emerged as a convenient tool to mine and learn from networked data. As a result, it is of interest to develop HIN embedding methods. Despite recent breakthroughs in HIN embedding methods, little research attention has been paid to exploit the relation semantics in HINs and further integrate it to improve the embedding quality. Considering the sophisticated correlations in HINs, we in this paper propose a novel HIN embedding method LRHNE to yield latent-relation enhanced embeddings for nodes. Our work mainly involves three contributions: i) we verify that the latent relation can promote the embedding quality indeed through a real-world dataset, then a novel graph inception network is proposed to extract the latent relational features under the guidance of partial prior knowledge; ii) taking into account the existing structure information and inferred latent relation knowledge, we propose a cross-aligned variational graph autoencoder to extract and further fuse both the structure and latent relational features into the embeddings; and iii) we perform extensive experiments to validate our proposed LRHNE, and experimental results show that our LRHNE can significantly outperform state-of-the-art methods. The multi-facet inspections also exhibit our method is robust and hyper-parameter insensitive, therefore, our method can serve as a radical tool to tackle the relation-sophisticated HINs.

Collective classification, as an important technique to study networked data, aims to exploit the label autocorrelation for a group of inter-connected entities with complex dependencies. As the emergence of various heterogeneous information networks (HINs), collective classification at present is confronting several severe challenges stemming from the heterogeneity of HINs, such as complex relational hierarchy, potential incompatible semantics and node-context relational semantics. To address the challenges, in this paper, we propose a novel heterogeneous graph convolutional network-based deep learning model, called HGCN, to collectively categorize the entities in HINs. Our work involves three primary contributions: i) HGCN not only learns the latent relations from the relation-sophisticated HINs via multi-layer heterogeneous convolutions, but also captures the semantic incompatibility among relations with properly-learned edge-level filter parameters; ii) to preserve the fine-grained relational semantics of different-type nodes, we propose a heterogeneous graph convolution to directly tackle the original HINs without any in advance transforming the network from heterogeneity to homogeneity; iii) we perform extensive experiments using four real-world datasets to validate our proposed HGCN, the multi-facet results show that our proposed HGCN can significantly improve the performance of collective classification compared with the state-of-the-art baseline methods.

IoT devices provide a rich data source that is not available in the past, which is valuable for a wide range of intelligence applications, especially deep neural network (DNN) applications that are data-thirsty. An established DNN model provides useful analysis results that can improve the operation of IoT systems in turn. The progress in distributed/federated DNN training further unleashes the potential of integration of IoT and intelligence applications. When a large number of IoT devices are deployed in different physical locations, distributed training allows training modules to be deployed to multiple edge data centers that are close to the IoT devices to reduce the latency and movement of large amounts of data. In practice, these IoT devices and edge data centers are usually owned and managed by different parties, who do not fully trust each other or have conflicting interests. It is hard to coordinate them to provide end-to-end integrity protection of the DNN construction and application with classical security enhancement tools. For example, one party may share an incomplete data set with others, or contribute a modified sub DNN model to manipulate the aggregated model and affect the decision-making process. To mitigate this risk, we propose a novel blockchain based end-to-end integrity protection scheme for DNN applications integrated with an IoT system in the edge computing environment. The protection system leverages a set of cryptography primitives to build a blockchain adapted for edge computing that is scalable to handle a large number of IoT devices. The customized blockchain is integrated with a distributed/federated DNN to offer integrity and authenticity protection services.

Network embedding aims at learning the latent representations of nodes while preserving the complex structure of the underlying graph. Real-world networks are usually related with each other via common nodes, the so-called multiplex network. To make the data mining work on the multiplex network more actionable, it become urgent and essential to transform it into low-dimension vector space. Recently, several works have been proposed to leverage the complementary information for embedding. However, they suffer from sacrificing distinct properties of the counterparts in different layers, as they preserve much noise information into embedding vectors. In this paper, we propose a Noise-Aware Network Embedding approach for Multiplex Network, namely NANE. Unlike previous works, NANE considers the roles of an identical node in different layers, and adopts a more robust and flexible strategy to rationally integrate the cross-layer information while keeping the unique characteristic of each layer. We perform extensive evaluations on several real-world datasets. The experimental results demonstrate that our NANE can achieve better performance on link prediction task and significantly outperform previous methods especially in noisy multiplex network scenarios.

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes across different networks, i.e., so-called network alignment. In this paper, we propose a cross-network embedding method CrossMNA for multi-network alignment problem through investigating structural information only. Unlike previous methods focusing on pair-wise learning and holding the topology consistent assumption, our proposed CrossMNA considers the multi-network scenarios which involve at least two types of networks with diverse network structures. CrossMNA leverages the cross-network information to refine two types of node embedding vectors, i.e., inter-vector for network alignment and intra-vector for other downstream network analysis tasks. Finally, we verify the effectiveness and efficiency of our proposed method using several real-world datasets. The extensive experiments show that our CrossMNA can significantly outperform the existing baseline methods on multi-network alignment task, and also achieve better performance for link prediction task with less memory usage.

In Software-Defined Networking (SDN), Ternary Content Addressable Memory (TCAM) enables fast lookup with flexible wildcard rule patterns for flow tables, however, the scarcity and expensiveness of TCAM dramatically limit the number of rules that switches can support. Rule caching for TCAM breaks the flow table size constraint by appropriate combinations of hardware and software processing. Nevertheless, previous literatures, from the viewpoint of maximizing cache hit ratio, ignore the TCAM update operations incurred by cache replacement, which is severely time-consuming. In this paper, we solve the TCAM cache replacement problem from a standpoint of reducing update cost. Upon comprehensively analyze and measure on cache hit ratio and update cost while sticking to the rule dependency constraints, we propose an effective cache replacement algorithm that can dynamically and adaptively adjust TCAM rules on a switch. Our experimental results show that our proposed algorithm can effectively eliminate over 60% update operations with less than 5% cache hit loss.

Delegated Proof-of-Stake (DPoS) is an efficient, decentralized, and flexible consensus framework available in the blockchain industry. However, applying DPoS to the decentralized Internet of Things (IoT) applications is quite challenging due to the nature of IoT systems such as large-scale deployments and huge amount of data. To address the unique challenge for IoT based blockchain applications, we present Roll-DPoS, a randomized delegated proof of stake algorithm. Roll-DPoS inherits all the advantages of the original DPoS consensus framework and further enhances its capability in terms of decentralization as well as extensibility to complex blockchain architectures. A number of modern cryptographic techniques have been utilized to optimize the consensus process with respect to the computational and communication overhead.

In the big data community, Spark has been widely used for processing interactive queries. Spark employs a query optimizer, called Catalyst, to provides a set of optimization rules and supports Cost-Based Optimization (CBO). In this paper, we investigated the effectiveness of the optimization rules and cost-based optimization in Catalyst. We conducted comprehensive validation experiments by varying the data volume and cluster scale, and found that the execution time of most TPC-H queries were reduced slightly even when query optimizations are applied. We derived some interesting observations on Catalyst, which can help the community better understand and improve the query optimizer of Spark in future.

With recent studies in Searchable Symmetric Encryption (SSE), a client can efficiently perform keyword queries over its outsourced data on a remote but untrusted server (e.g., a public cloud), and correctly retrieve associated files without revealing the confidentiality of his/her data. Besides keyword search, many recent schemes also studied range queries on encrypted data, where range search is also one of the most extensively used queries in databases and information retrieval. However, most of these previous works supporting range search are neither efficient nor compatible with existing keyword SSE schemes. In this paper, we propose two range SSE schemes to enable range queries on encrypted data. Both of our schemes are not only efficient, but also highly compatible with existing keyword SSE schemes. Specifically, the search time of our first scheme is extremely efficient when the values in a range query are sparsely presenting in a dataset; while our second design can achieve an optimal token size and significantly save token generation costs. Moreover, we rigorously define and analyze the security of our schemes, and also conduct extensive experiments with a real dataset to demonstrate the performance of our schemes.

The integration of cloud computing and Internet of Things (loT) is quickly becoming the key enabler for the digital transformation of the healthcare industry by offering comprehensive improvements in patient engagements, productivity and risk mitigation. This paradigm shift, while bringing numerous benefits and new opportunities to healthcare organizations, has raised a lot of security and privacy concerns. In this paper, we present a reliable, searchable and privacy-preserving e-healthcare system, which takes advantage of emerging cloud storage and IoT infrastructure and enables healthcare service providers (HSPs) to realize remote patient monitoring in a secure and regulatory compliant manner. Our system is built upon a novel dynamic searchable symmetric encryption scheme with forward privacy and delegated verifiability for periodically generated healthcare data. While the forward privacy is achieved by maintaining an increasing counter for each keyword at an IoT gateway, the data owner delegated verifiability comes from the combination of the Bloom filter and aggregate message authentication code. Moreover, our system is able to support multiple HSPs through either data owner assistance or delegation. The detailed security analysis as well as the extensive simulations on a large data set with millions of records demonstrate the practical efficiency of the proposed system for real world healthcare applications.

Match field overlaps are common in most of today's networks because of wildcards and Longest Prefix Match (LPM). These overlaps exacerbate the loop-free update problem in Software-Defined Networks (SDNs). Because with overlaps, forwarding loops exist not only between old and new routes of a single policy but also among routes of different policies. However, previous work can only eliminate forwarding loops introduced by a single policy. In this paper, we focus on eliminating forwarding loops introduced by match field overlaps of multiple policies. Moreover, we prove that it is NP-hard to give a (ks+ 1)-round schedule, where ksis the maximum round number of the single policy schedules. We then propose an N-ary tree based heuristic algorithm that efficiently produces a schedule with approximate minimum rounds. Experimental results show that our approach could reduce more than 90% unnecessary rounds and achieve absolute loop-freedom especially when updating policies with overlaps.

As advanced computing and communication technologies penetrate every aspect of our life, we have witnessed the persistent growth of open systems where entities interact with one another without prior knowledge or experiences. Trust becomes an important metric in such open systems. This paper presents a dependable trust management scheme-GroupTrust, and a working system to support GroupTrust. It makes three original contributions. First, we identify a set of vulnerabilities that are common in existing reputation based trust models. We show that reputation trust built solely on direct experiences or by combining direct experiences with uniform trust propagation can be vulnerable. Second, we develop GroupTrust, a dependable trust management scheme to provide reliable trust management in the presence of dishonest ratings, malicious camouflage, and malicious collusive behaviors. The GroupTrust scheme is novel in two aspects: (i) we develop a pairwise similarity based feedback credibility to enhance the resilience of trust computation in the presence of dishonest ratings; (ii) we propose to propagate trust based on a Susceptible-Infected-Recovered (SIR) model, which defines trust propagation threshold to control how trust should be propagated. Finally, we evaluate the effectiveness of GroupTrust against fourthreat models using both simulated and real world datasets. Our experimental results show that feedback credibility based local trust computation can effectively constrain strategically malicious participants from taking advantages of their dishonest ratings. SIR-based trust propagation control enables safe trust propagation and blocks irrational trust propagation. We show that GroupTrust scheme significantly outperforms other trust models in terms of both performance and attack resilience in the presence of dishonest feedbacks, sparse feedbacks, and strategically malicious participants against four representative threat models.

The Internet of Things (IoT) is expected to become a global information and communication infrastructure for cyber physical systems and to bring numerous value-added services for modern society. However, the integration of heterogeneous devices and service models into a cohesive system significantly increases the complexity of design and deployment and introduces the new challenges for the security of systems and processed as well as the privacy of the collected data. The Workshop on Trustworthy Embedded Devices (TrustED) addresses all aspects of security and privacy related to embedded systems and the IoT. TrustED 2016 is a continuation of previous workshops in this series, which were held in conjunction with ESORICS 2011, IEEE Security & Privacy 2012, ACM CCS 2013, ACM CCS 2014, and ACM CCS 2015 (see http://www.trusted-workshop.de for details). The goal of this workshop is to bring together experts from academia and research institutes, industry, and government in the field of security and privacy in cyber physical systems to discuss and investigate the problems, challenges, and recent scientific and technological developments.

With the advent of ubiquitous computing and the Internet of Things (IoT), the security and privacy issues for various smart devices such as radio-frequency identification (RFID) tags and wireless sensor nodes are receiving increased attention from academia and industry. A number of lightweight cryptographic primitives have been proposed to provide security services for resource-constrained smart devices. As one of the core primitives, a cryptographically secure pseudorandom number generator (PRNG) plays an important role for lightweight embedded applications. The most existing PRNGs proposed for smart devices employ true random number generators as a component, which generally incur significant power consumption and gate count in hardware. In this article, we present Warbler family, a new pseudorandom number generator family based on nonlinear feedback shift registers (NLFSRs) with desirable randomness properties. The design of the Warbler family is based on the combination of modified de Bruijn blocks together with a nonlinear feedback Welch-Gong (WG) sequence generator, which enables us to precisely characterize the randomness properties and to flexibly adjust the security level of the resulting PRNG. Some criteria for selecting parameters of the Warbler family are proposed to offer the maximum level of security. Two instances of the Warbler family are also described, which feature two different security levels and are dedicated to EPC C1 Gen2 RFID tags and wireless sensor nodes, respectively. The security analysis shows that the proposed instances not only can pass the cryptographic statistical tests recommended by the EPC C1 Gen2 standard and NIST but also are resistant to the cryptanalytic attacks such as algebraic attacks, cube attacks, time-memory-data tradeoff attacks, Mihaljević et al.’s attacks, and weak internal state and fault injection attacks. Our ASIC implementations using a 65nm CMOS process demonstrate that the proposed two lightweight instances of the Warbler family can achieve good performance in terms of speed and area and provide ideal solutions for securing low-cost smart devices.

We propose two practical and provably secure password hashing algorithms, Pleco and Plectron. They are built upon well-understood cryptographic algorithms, and combine advantages of symmetric and asymmetric primitives. By employing the Rabin cryptosystem, we prove that the one-wayness of Pleco is at least as strong as the hard problem of integer factorization. In addition, both password hashing algorithms are designed to be sequential memory-hard, in order to thwart large-scale password cracking by parallel hardware, such as GPUs, FPGAs, and ASICs. Moreover, the total computation and memory consumptions of Pleco and Plectron are tunable through their cost parameters.

Distributed service networks are popular platforms for service providers to offer services to consumers and for service consumers to acquire services from unknown parties. eBay and Amazon are two well-known examples of enabling and hosting such service networks to connect service providers to service consumers. Trust management is a critical component for scaling such distributed service networks to a large and growing number of participants. In this article, we present ServiceTrust ++ , a feedback quality--sensitive and attack resilient trust management scheme for empowering distributed service networks with effective trust management capability. Compared with existing trust models, ServiceTrust ++ has several novel features. First, we present six attack models to capture both independent and colluding attacks with malicious cliques, malicious spies, and malicious camouflages. Second, we aggregate the feedback ratings based on the variances of participants’ feedback behaviors and incorporate feedback similarity as weight into the local trust algorithm. Third, we compute the global trust of a participant by employing conditional trust propagation based on the feedback similarity threshold. This allows ServiceTrust ++ to control and prevent malicious spies and malicious camouflage peers from boosting their global trust scores by manipulating the feedback ratings of good peers and by taking advantage of the uniform trust propagation. Finally, we systematically combine a trust-decaying strategy with a threshold value--based conditional trust propagation to further strengthen the robustness of our global trust computation against sophisticated malicious feedback. Experimental evaluation with both simulation-based networks and real network dataset Epinion show that ServiceTrust ++ is highly resilient against all six attack models and highly effective compared to EigenTrust, the most popular and representative trust propagation model to date.

ABSTRACT Trust mechanism has become a research focus in recent years as a novel and valid way to ensure the transaction security in peer‐to‐peer file sharing networks. Nevertheless, some fundamental challenges still exist, for example: How can malicious peers be effectively isolated? How can various threats of manipulation by strategic peers be resisted? What strategy should be used to ensure that the service providers are authentic peers? Considering these challenges in our minds, in this paper, we propose a new trust mechanism based on the maximum flow theory. We firstly add a few prestigious peers into a cluster as the original members according to their transaction behaviors in a period; then, we perform maximum flow algorithm and identify those peers that still link from (to) the peers in the cluster as new members, which is carried out repeatedly, and almost every normal peer would finally become the member of the cluster. Each request peer has the priority to select downloading sources from this cluster according to our trust mechanism. In this way, the malicious peers are isolated, and their transaction behaviors are also confined largely even though they have high reputation. Extensive experimental results confirm the efficiency of our trust mechanism against the threats of exaggeration, cheat, collusion, and disguise. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT In this paper, we present attribute‐based ring signcryption scheme, which realizes the concept of ring signcryption in the attribute‐based encryption frame firstly. In our system, it allows a user to signcrypt a message by a set of attributes that are chosen without revealing its identity. In additional, we propose the security models and prove the confidentiality and unforgeability of our schemes. We also present the efficiency of our scheme by comparisons. Copyright © 2012 John Wiley & Sons, Ltd.

This paper argues that trust and reputation models should take into account not only direct experiences (local trust)and experiences from the circle of ”friends”, but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrus

Hummingbird is a recently proposed lightweight cryptographic algorithm for securing RFID systems. In 2011, Saarinen reported a chosen-IV, chosen-message attack on Hum- mingbird in FSE'll. In this paper, we propose a lightweight remedial scheme in response to the Saarinen's attack. The scheme is quite efficient both in software and hardware since only two cyclic shifts are involved. Using this simple tweak, we can keep the compact design of Hummingbird as well as enhance the security of Hummingbird. Readers are welcome to attack the remedial Hummingbird.

Abstract The purpose of this paper is to introduce a technique to eliminate parallel attacks to protocol in virtual organization (VO) through enforcing dynamic authorization policies. Grid realizes coordinated resource sharing across multiple management domains. VO is defined as a key concept for operation and management of grid services. Due to the fact that VO focuses on dynamic, cross‐organizational sharing relationships, one of the central challenges in the construction of scalable VO is that protocol specified by VO may have process of parallel running. To solve this problem, we present a formal definition of non‐honest participants' malicious coordination operations which are necessary for parallel attack counterexample in VO. Based on that, we present the two‐level dynamic authorization policy deploying scheme in VO for eliminating parallel attacks. Copyright © 2011 John Wiley & Sons, Ltd.

The absence of an online trusted authority makes the issue of key revocation in mobile ad hoc networks (MANETs) particularly challenging. In this paper, we present a novel self-organized key revocation scheme based on the Dirichlet multinomial model and identity-based cryptography (IBC). Our key revocation scheme offers a theoretically sound basis for a node in MANETs to predict the behavior of other nodes based on its own observations and reports from peers. In our scheme, each node keeps track of three categories of behavior defined and classified by an external trusted authority, and updates its knowledge about other nodespsila behavior with 3-dimension Dirichlet distribution. Differentiating between suspicious behavior and malicious behavior enables nodes to make multilevel response by either revoking keys of malicious nodes or ceasing the communication with suspicious nodes for some time to gather more information for making further decision. Furthermore, we also analyze the attack-resistant properties of our key revocation scheme through extensive simulations in the presence of adversaries.

The ideal class groups of hyperelliptic curves (HECs) can be used in cryptosystems based on the discrete logarithm problem. Recent developments of computational technologies for scalar multiplications of divisor classes have shown that the performance of hyperelliptic curve cryptosystems (HECC) is compatible to that of elliptic curve cryptosystems. Especially, due to short operand sizes, genus 3 HECC are well suited for all kinds of embedded processor architectures, where resources such as storage, time or power are constrained. In the paper, the acceleration of the divisor class doubling for genus 3 HECs over binary fields is investigated and the number of field operations needed is analysed. By constructing birational transformations of variables, four types of curves which can lead to much faster divisor class doubling are found and the corresponding explicit formulae are given. In particular, for special genus 3 HECs over binary fields with h(X)=1, the fastest explicit doubling formula published so far which only requires one field inversion, ten field multiplications and eleven field squarings, is obtained. Furthermore, comparisons with the known results in terms of field operations and implementations of genus 3 HECC over three different binary fields on a Pentium-4 processor are provided.

In [H. Oguro et al., (2003)], the authors proposed efficient algorithms for the /spl tau/-adic sliding window method and applied the algorithms to Koblitz elliptic curve cryptosystem. In this paper, we extend their ideas to hyperelliptic curve cryptosystem. We give respectively explicit formulae of simultaneous divisor class addition-subtraction algorithm for genus 2 hyperelliptic curves in affine and projective coordinate system and analyse the case of genus 3 hyperelliptic curves. Using this idea and Montgomery trick, we can reduce the number of inversions, multiplications and squares. In addition, we apply the idea to speed up the precomputation part of two scalar multiplication algorithms for hyperelliptic curve cryptosystem and discuss the efficiency of improved algorithms in detail.