Zero-Knowledge Proofs (ZKPs), especially zk-SNARKs, rely heavily on Multi-Scalar Multiplication (MSM), a compute-intensive elliptic curve operation. While prior work targets curves with optimized operations like BLS12-377, MSM on general-purpose curves such as BLS12-381 remains challenging due to imbalanced resource usage, performance gaps between curve operations, and low utilization of point addition. This paper proposes FlexMSM to support scalable MSM cores on a single FPGA for BLS12-381 curve, delivering significant gains over existing works for input sizes from 218 to 226 .

Low Earth Orbit (LEO) satellite constellations have experienced rapid growth in recent years, driven by their potential to deliver global, high-bandwidth Internet services with low latency. Beyond connectivity, LEO constellations also offer promising opportunities to enable in-orbit processing of space-native data to support a wide range of emerging space applications. In this context, the concept of space computing has been proposed, a paradigm that seamlessly integrates networking and computing to provide computing-as-a-service anytime and anywhere in space. However, the inherent characteristics of satellite constellations, such as dynamic network topologies, constrained system resources, and the harsh space environment, pose significant challenges in achieving this vision. This paper outlines the system architecture and the key enabling technologies for space computing, including spaceborne computers, laser communications, spaceborne router, distributed operating systems, and onboard AI. We also present the implementation of an open space computing platform, the 3-Body computing constellation, along with the in-orbit experimental results that demonstrate the advantages of multi-satellite distributed computing. Furthermore, we outline future research directions essential for advancing toward a truly interconnected, autonomous, and intelligent space computing system.

In the field of artificial intelligence, secure model inference is essential for protecting data confidentiality, which allows users to interact with trained models for decision-making support without privacy leakage. However, current secure inference methods often overlook the simultaneous verification of data origins for both user inputs and model weights, which is crucial for maintaining the integrity of inference outcomes. In this study, we present a novel verifiable secure inference scheme that leverages blockchain to enhance the verifiability of both the inference process and the origins of user inputs and model weights. We integrate the decentralized ledger to store the committed inputs and weights, serving as convincing data origins. We then transform neural networks into zero-knowledge proof constraints with optimized structures for the inference process. To illustrate its application scenario, we propose a regulatable privacy-preserving transaction scheme. Its regulation depends on anomaly detection on private transactions without privacy leakage, which takes the encrypted ledger as the data source and the committed detection model as the parameter source to perform our verifiable secure inference. We provide rigorous security proofs for our schemes, demonstrating their authenticity and privacy. We implement them to demonstrate their scalability through analyzing their computational and communication performance.

Federated Learning, as a multi-party machine learning paradigm, has garnered significant attention, but model updates may still leak sensitive information. Secure aggregation protocols are considered an effective solution for privacy protection in Federated Learning. However, in large-scale federated learning systems, designing efficient and practical secure aggregation remains a critical challenge. Moreover, while secure aggregation effectively conceals model updates, it unintentionally complicates the detection and mitigation of poisoning attacks, thereby exposing the system to vulnerabilities from both data and model poisoning. To address these challenges, we propose ClusterGuard, a secure clustered aggregation scheme. ClusterGuard leverages Verifiable Random Function (VRF) to ensure fair and transparent client clustering. Within each cluster, it employs a lightweight key-homomorphic masking mechanism combined with verifiable secret sharing to enable secure and efficient aggregation. Fur thermore, we design a dual filtering mechanism based on cosine similarity and norm to effectively detect and resist poisoning attacks. We provide two variants of ClusterGuard for both client-server and decentralized environments with blockchains, respectively. Extensive experiments on standard datasets demonstrate that ClusterGuard achieves over 2× efficiency improvement compared to advanced secure aggregation methods. Even with 20% of clients being malicious, the trained model maintains accuracy comparable to the original model, outperforming state-of-the art robustness solutions. ClusterGuard provides a more efficient, secure, and robust solution for practical federated learning.

Modular Inversion (MI) is one of the fundamental arithmetic operations in the finite field, which plays an essential role in various cryptographic applications and requires high performance and security. Unfortunately, the simple MI algorithm is vulnerable to side-channel attacks, such as the timing attack, which can compromise the cryptographic system by analyzing the time taken to execute cryptographic algorithms. Attackers may recover the initial data since the time can differ based on the input. Besides, the low complexity and low resource consumption of hardware implementations in MI are also challenging. In this article, we propose two novel modular inversion algorithms, named Constant-Time Integer Modular Inversion (CT-IMI) and Constant-Time Complementary Montgomery Modular Inversion (CT-CMMI). They both consist of constant iteration rounds to resist the timing attack. CT-IMI processes the data in the integer field, which is designed for common scenarios. CT-CMMI is suitable for the cross-domain case, which can directly use data in the Montgomery domain and avoid the conversion steps for some specific applications, e.g., scalar multiplication in Elliptic Curve Cryptography (ECC). In software simulations, we measure the average clock cycles for a single inversion and illustrate the relationship between various bit lengths and the latency. The significant differences between constant and non-constant algorithms demonstrate the vulnerability of modular inversion to timing attacks. In addition, we design two efficient hardware architectures on FPGA. Experimental results show that our CT-IMI can finish a single inversion in 2.56 \(\mu\) s with 4.2k LUTs, 1.8k FFs, and our CT-CMMI requires 2.45 \(\mu\) s with 2.7k LUTs, 1.6k FFs. The product of area and latency of our CT-IMI and CT-CMMI can reach 10.50 and 6.62, respectively, which shows optimal performance compared with all the results in the existing literature.

Cross-chain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration services for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) solutions for cross-chain data exchange across multiple domains are constrained by synchronous networks, hindering efficient data exchange in intermittent network environments. Furthermore, there is a lack of research on asynchronous cross-chain transaction pool mechanisms, which are crucial for optimizing system utility. In this paper, we propose AC-BaaS, anasynchronouscross-blockchainasaservice framework tailored for the multi-domain IoT. Built upon a specially designed asynchronous sidechain architecture, the system leverages a committee to provide AC-BaaS for data exchange across multiple IoT domains. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We further propose a heuristic for optimizing the utility of the buffer pool mechanism to strike a balance between performance and resource consumption. We also examine DAS delay size settings to trade-off security and efficiency. We analyze and prove the security of AC-BaaS, simulate asynchronous communication environments under various security levels, and conduct a comprehensive evaluation. The results show that AC-BaaS outperforms SOTA schemes, improving throughput by an average of 1.71 to 5.09 times, reducing transaction latency by 64.36% to 85.49%, and maintaining comparable resource overhead.

Despite the rapid development of Chinese vision-language models (VLMs), most existing Chinese vision-language (VL) datasets are constructed on Western-centric images from existing English VL datasets. The cultural bias in the images makes these datasets unsuitable for evaluating VLMs in Chinese culture. To remedy this issue, we present a new Chinese Vision-Language Understanding Evaluation (CVLUE) benchmark dataset, where the selection of object categories and images is entirely driven by Chinese native speakers, ensuring that the source images are representative of Chinese culture. The benchmark contains four distinct VL tasks ranging from image-text retrieval to visual question answering, visual grounding and visual dialogue. We present a detailed statistical analysis of CVLUE and provide a baseline performance analysis with several open-source multilingual VLMs on CVLUE and its English counterparts to reveal their performance gap between English and Chinese. Our in-depth category-level analysis reveals a lack of Chinese cultural knowledge in existing VLMs. We also find that fine-tuning on Chinese culture-related VL datasets effectively enhances VLMs' understanding of Chinese culture.

Federated learning (FL) allows each client to train data locally and share only model parameters with an aggregation server. A critical component of FL is secure aggregation (SA), which protects user privacy during the server-side aggregation of client model parameters. However, SA-based FL still faces several challenges from a malicious server, affecting both performance and security. To address these issues, we propose the first 2-round-trip verifiable and oblivious aggregation protocol. Specifically, our protocol incorporates: (1) a consistent temporary key disclosure mechanism for round-trip-efficient communication with client dropout resilience; (2) a message authentication code to verify aggregation results; and (3) a multi-round oblivious aggregation scheme that conceals both individual and global model parameters. These three key techniques collectively enable efficient communication while preventing a malicious server from tampering with aggregation results and accessing any individual or global parameters. We conducted a comprehensive evaluation to demonstrate the practicality and efficiency of our design compared to existing schemes.

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines.

Decentralized finance based on blockchain has experienced rapid development. To safeguard the privacy of participants, decentralized anonymous payment (DAP) systems such as ZCash and Zether have emerged. These systems employ cryptographic techniques to conceal the trader addresses and payment amounts. However, this anonymity presents challenges in terms of regulation. To address this issue, we propose the Walsh-DAP (WDAP) scheme, an efficient and generic regulation scheme for decentralized anonymous payments that strikes a balance between regulation and privacy preservation. Our scheme introduces two regulation policies: first, users who have exceeded their spending limits within a certain period will be identified during the regulation process; second, the supervisor possesses the capability to trace any anonymous transaction. To implement regulation effectively, we have designed an innovative commitment scheme, Walsh commitment, which leverages the orthogonal properties of Walsh codes to achieve the features of aggregatability and extractability. The supervisor in WDAP only needs to deal with the aggregation result of the Walsh commitments instead of the huge amount of raw transactions information, which greatly increases the efficiency. In a DAP system with 256 users, 10 transactions per second and 30 days as a regulation period, we reduced the communication cost for regulation from 14 GB to 94.20 KB, and the computing cost from$\text{1.6}\times \text{10}^{\text{5}}$s to 2.17s. Both improvement is of over five orders of magnitude. We formally discussed the security of the whole system, and verified its feasibility and practicability in the ZCash system.

Federated learning enables collaborative model training while preserving data privacy by keeping data local. To protect user privacy during model aggregation, secure aggregation (SA) protocols are widely adopted to mask models. However, existing SA protocols require at least three round trips per aggregation and lack mechanisms to verify aggregation results. Verifiable SA addresses the verification gap but incurs high communication costs. TEE-based SA minimizes round trips but faces computational bottlenecks due to TEE's limited physical memory, especially when handling larger models or numerous clients. In this work, we introduce OPSA, an efficient and verifiable one-pass SA protocol based on TEE. By handling client dropouts via server-side TEE, OPSA enables the server to aggregate masked models in a single pass, significantly reducing round trips. To mitigate TEE's limitations, OPSA offloads tasks like model aggregation and mask elimination outside TEE, with only shared keys processed within TEE. Building on this design, we propose KhPRF-OPSA (single masking) and POT-OPSA (double masking) protocols, both incorporating novel cryptographic primitives. Furthermore, OPSA integrates commitment and signature mechanisms to ensure result verifiability with only$O(1)$additional communication overhead per client. Compared to state-of-the-art schemes, OPSA achieves a 2$\sim 10\times$speedup in multi-round aggregation while guaranteeing result verification.

Authenticated Encryption with Associated-Data (AEAD) can ensure both confidentiality and integrity of information in encrypted communication. Distinctive variants are customized from AEAD to satisfy various requirements. In this paper, we take a 128-bit lightweight AEAD stream cipher Fountain as an example. We provide a general cryptographic solution with three Fountain variants. These three variants are for encryption, message authentication code (MAC) generation, and authenticated encryption with associated data, respectively. Besides, we propose area-saved and throughput-improved strategies for the FPGA implementation of Fountain. The conventional paralleled hardware implementation leads to much resource-consuming with higher parallel width. We propose a hybrid architecture with parallel and serial update modes simultaneously. We also analyze the trade-off between area occupation and authentication latency for those two architectures. According to our discussion, hybrid architectures can perform efficiently with higher throughput than most ciphers, including Grain-128 x32. Our Fountain keystream generator occupies 46 slices on Spartan-3 FPGAs, smaller than most ciphers with the same security level, and even smaller than the 80-bit security level cipher Trivium. In summary, the customized Fountain with optimized implementations on FPGA is suitable for various applications in the field of IoT.

Zero-knowledge range proof (ZKRP) asserts that a committed integerVlies in a given range like$[{0, 2^{n}-1}]$without other leakages ofV. It is vital in various privacy-preserving systems. Moving forward, the quest for post-quantum security is still in its infancy; the proof size of state-of-the-art lattice-based ZKRP (Lyubashevsky et al., CCS 20 and Couteau et al., Eurocrypt 21) remains linear inn, directly impacting the long-term sustainability in applications such as immutable ledgers. Confronting this unresolved impasse, we propose SHARP-PQ,i.e., succinct hash-based arbitrary-range proof with post-quantum security. SHARP-PQ offers proof size poly-logarithmic ton, optimized batch proofs, and versatile (new) capabilities. Its success stems from the improved inner product argument and exploitation of homomorphism. Empirically, SHARP-PQ features at least$10\times $smaller proof size for multiple ranges over lattice-based ZKRPs while maintaining competitive prover and verifier times. SHARP-PQ also outperforms ZKRPs directly constructed from hash-based generic zero-knowledge proofs at most$10 \times $.

The recent popularity of cryptocurrencies like Bitcoin and Ethereum has drawn widespread attention to the blockchain technique. In particular, some private cryptocurrencies like Zerocash and Monero enhance privacy protection by concealing the identities of participants and transaction amounts. However, such comprehensive privacy measures present regulatory challenges to malicious activities like money laundering and extortion. Therefore, building a novel blockchain that maintains privacy while supporting regulatory oversight is crucial. In this paper, we propose a regulatable and privacy-preserving blockchain scheme that introduces a decoupled and preparatory regulatory process. It serves as a privacy-preserving first line of defense, enabling the identification of anomalous transactions without compromising the confidentiality of the underlying data. Our approach pioneers a method for anomaly screening on private transactions, mitigating risks without resorting to key escrow or content recovery, thus preserving end-to-end privacy for legitimate users. Initially, we explore suitable transaction features within private blockchains for training machine learning classifiers to detect anomalous behaviors. Subsequently, we customize a privacy-centric classifier employing homomorphic encryption to achieve private computation of anomaly detection without leaking sensitive information from private transaction content. We then construct the zero-knowledge proof for validating the encrypted computation process. Our work pioneers in fully integrating homomorphic encryption with zero-knowledge proof, enabling credible and trustworthy verification of the homomorphic ciphertext computations. Finally, we conduct comprehensive security analysis and experimental simulations. The experimental results demonstrate the efficiency and scalability of our approach.

Blockchain layer 2 solutions aim to address scalability issues in Layer 1 networks by improving transaction efficiency and alleviating congestion. The rollup, a well-known Layer 2 scaling protocol, uses an aggregate signature scheme based on the succinct non-interactive argument of knowledge (SNARG) to package transactions. The further promotion of rollup faces the challenge of balancing computation efficiency and communication costs. In addition, with the continuous development of quantum computing, a transition to post-quantum cryptography is considered crucial for long-term security. Our main contribution is an aggregate Falcon signature scheme for post-quantum rollup based on a novel SNARG scheme. The proposed SNARG is based on the Plonkish circuit with enhanced custom gates, referred to as the ECG circuit, and a post-quantum multilinear polynomial commitment scheme (PolyCom). The former can represent more complex operations while also controlling the witness scale. The latter realizes quantum-resistant security for the proposed SNARG and the aggregate signature. In comparison to the aggregate signature based on Orion, our scheme achieves lower aggregation and communication costs. Performance analysis indicates a 38 % decrease in aggregation time and a 88 % decrease in communication costs. As an additional contribution, we introduce a novel polynomial interactive oracle proof (PolyIOP) protocol for the ECG circuit, which can combine with a multilinear PolyCom scheme to form a SNARG protocol with lower computation and communication overhead compared to existing schemes.

This paper studies robust nonparametric regression, in which an adversarial attacker can modify the values of up to q samples from a training dataset of size N. Our initial solution is an M-estimator based on Huber loss minimization. Compared with simple kernel regression, i.e. the Nadaraya-Watson estimator, this method can significantly weaken the impact of malicious samples on the regression performance. We provide the convergence rate as well as the corresponding minimax lower bound. The result shows that, with proper bandwidth selection, supremum error is minimax optimal. The L2 error is optimal with relatively small q, but is suboptimal with larger q. The reason is that this estimator is vulnerable if there are many attacked samples concentrating in a small region. To address this issue, we propose a correction method by projecting the initial estimate to the space of Lipschitz functions. The final estimate is nearly minimax optimal for arbitrary q, up to a logarithmic factor.

Federated learning systems are susceptible to adversarial attacks. To combat this, we introduce a novel aggregator based on Huber loss minimization, and provide a comprehensive theoretical analysis. Under independent and identically distributed (i.i.d) assumption, our approach has several advantages compared to existing methods. Firstly, it has optimal dependence on epsilon, which stands for the ratio of attacked clients. Secondly, our approach does not need precise knowledge of epsilon. Thirdly, it allows different clients to have unequal data sizes. We then broaden our analysis to include non-i.i.d data, such that clients have slightly different distributions.

A range proof serves as a protocol for the prover to prove to the verifier that a committed number lies in a specified range, such as [0,2n), without disclosing the actual value. Range proofs find extensive application in various domains. However, the efficiency of many existing schemes diminishes significantly when confronted with batch proofs encompassing multiple elements.

Federated recommendation (FR) is a decentralised approach to training personalised recommender systems, protecting users' privacy by avoiding data collection. Despite its privacy advantages, FR remains vulnerable to poisoning attacks. We focus on untargeted poisoning attacks against FR which degrade the overall performance of recommender services, leading to a detrimental impact on user experience and service quality. In this paper, we propose a general framework to formalise untargeted attacks and identify the vital role played by the interplay between items and user profiles in determining FR's performance. We present an untargeted attack FRecAttack2 which exploits this interplay. Specifically, we develop various methods for sampling user profiles, which approximate user distributions with and without collusion among malicious users. Then we leverage a new measurement to identify items that can disrupt the original interplay with user profiles, based on the change velocity of items' recommendation scores during optimisation. Extensive experiments demonstrate the superiority of our attack, outperforming existing methods by up to 27.56%, and its stealthiness in evading mainstream defences. To counteract untargeted attacks, we present a defence GuardCQ to detect malicious users by quantifying their contribution to boost the right interplay between items and user profiles. Empirical results show that GuardCQ effectively mitigates the attack's impact on FR and enhances the robustness of FR against poisoning attacks.

The rapid development of the Internet of Things (IoT) has led to the generation and perception of large amounts of data from IoT devices. These data are outsourced to the cloud for flexible sharing and deep analytics, which can significantly enhance IoT applications. But this raises privacy concerns for IoT devices. Attribute-based encryption is applied to realize fine-grained access control, which offers data owners control capability over their outsourced data. However, the centralized infrastructure is susceptible to the single-point-of-failure problem and may not be suitable for highly distributed IoT applications due to high latency. To overcome this issue, blockchain technology is introduced to realize a distributed infrastructure and provide robust data services. Owing to the innate transparency characteristic of blockchain, challenges associated with privacy are amplified. Therefore, this article presents an efficient and decentralized data access control (EDDAC) scheme that preserves attribute privacy. The scheme utilizes chameleon hash to implement attribute hiding, providing resistance against dictionary attacks. Additionally, it includes blockchain-based decryption tests that reduce the decryption overhead on clients through the application of inner product predicate encryption. Furthermore, our scheme employs Shamir secret sharing to achieve decentralized authorization based on blockchain, thereby reducing the trust-building overhead on authorization nodes. Finally, we provide proof of the adaptive security of the proposed scheme and demonstrate its effectiveness and advantages through simulations and comparisons with existing literature.

In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

In the multi-turn dialogue reasoning task, existing models conduct word-level interaction on the entire context to gather reasoning evidence, which aims to select the logically correct one from the candidate response options. Observing the fact that the salient reasoning evidence usually comes from certain snippets of the whole dialogue session, one promising study direction is to explicitly identify the candidate reasoning contexts correlated with the dialogue reasoning options, called option-related contexts, and then make logical inference among them. However, such option-related contexts are stained with noisy information. As a result, existing models may reason unfairly with biased context and select wrong options. To tackle the context bias problem, in this article, we propose a novel CounterFactual learning framework for Dialogue Reasoning, named CF-DialReas, which mitigates the bias information by subtracting the counterfactual representation from the total causal representation. Specifically, we consider two scenarios, i.e., factual dialogue reasoning where the whole context is available to estimate the total causal representation, and the counterfactual dialogue reasoning, which firstly utilizes three different types of utterance selectors to select option-unrelatedcontext, and then only the option-unrelatedcontext is available to guess the counterfactual representation. Experimental results on two public dialogue reasoning datasets show that the model with our mechanism can obtain higher ranking measures, validating the effectiveness of counterfactual learning of CF-DialReas. Further analysis on the generality of CF-DialReas shows that our counterfactual learning mechanism is generally effective to the widely-used models.

The integration of external knowledge graphs into dialogue systems effectively mitigates the generation of generic and uninteresting responses. This approach, particularly the explicit modeling of conversation flows from related concept entities, facilitates the generation of semantically rich and informative responses. However, recent models guided by concept entity flows present two primary limitations: (1) a limited semantic understanding of the post message, which complicates the selection of highly relevant 1-hop concept entities, and (2) an inability to extract dynamic and diverse semantic relations between the post message and 2-hop concept entities. To address these issues, we introduce FluGCF, a novel model that fluently generates dialogues with coherent guidance from concept entity flows. FluGCF employs a ternary fusion to explicitly model multi-hop concept entity flows using a post-aware knowledge encoding mechanism. This mechanism learns semantic concept entity features from both word and sentence-level text features. Additionally, we design a corresponding ternary decoding mechanism that dynamically selects concept entities or words from the vocabulary to enhance fluency and diversity in dialogue generation. FluGCF, implemented in PyTorch, was extensively evaluated on a large-scale dataset, revealing that it surpasses baseline models, including the state-of-the-art knowledge-aware model ConceptFlow, by nearly 15% in terms of fluency. Furthermore, it demonstrated notable enhancements in coherence, diversity and informativeness.

Cloud computing has been widely applied in data storage, but cloud computing is not armed with an efficient integrity check mechanism for users to learn whether their large volumes of data have been kept intact by the cloud. The concept of proofs of retrievability (PoR) was introduced to address such an issue by enabling users to check the integrity of their data stored by the cloud. But PoR requires users to regularly send queries to the cloud, and its integrity check method cannot be extended to share the verification responsibility in the multi-user setting where different users store the same data to the cloud. With such concerns in mind, we put forth a notion called outsourced multi-user proofs of retrievability ($\mathtt {OMTPoR}$) which allows users with the same data stored by the cloud to share the information for the integrity check, and a third party is required to regularly check data integrity on behalf of users using the shared information. We give a concrete construction of$\mathtt {OMTPoR}$based on the homomorphic property of an existing property and analyze its security. To enforce honest integrity checks, we build the concrete$\mathtt {OMTPoR}$construction over the blockchain using smart contracts to guarantee the honesty of participants, yielding a decentralized outsourced multi-user PoR solution that utilizes the blockchain miners as the third parties. Furthermore, our solution enables the cloud server to obtain payment for the storage service if the PoR is verified by the miners. We fully implement the$\mathtt {OMTPoR}$scheme over the blockchain to evaluate its performance, which demonstrates obvious superiority over traditional PoR schemes without the detection of data duplication.

Federated Learning (FL) is widely used in various industries because it effectively addresses the predicament of isolated data island. However, eavesdroppers is capable of inferring user privacy from the gradients or models transmitted in FL. Homomorphic Encryption (HE) can be applied in FL to protect sensitive data owing to its computability over ciphertexts. However, traditional HE as a single-key system cannot prevent dishonest users from intercepting and decrypting the ciphertexts from cooperative users in FL. Guaranteeing privacy and efficiency in this multi-user scenario is still a challenging target. In this paper, we propose a secure and efficient Federated Learning scheme (SecFed) based on multi-key HE to preserve user privacy and delegate some operations to TEE to improve efficiency while ensuring security. Specifically, we design the first TEE-based multi-key HE cryptosystem (EMK-BFV) to support privacy-preserving FL and optimize operation efficiency. Furthermore, we provide an offline protection mechanism to ensure the normal operation of system with disconnected participants. Finally, we give their security proofs and show their efficiency and superiority through comprehensive simulations and comparisons with existing schemes. SecFed offers a 3x performance improvement over TEE-based scheme and a 2x performance improvement over HE-based solution.

Outsourced Proofs of Retrievability (OPoR) with private verification enables a third party verifier to periodically check cloud data on behalf of users. However, such a scheme requires the verifier to keep a copy of the user's data and generate tags for the data like the data owner. In other words, in addition to storing the data and tags from the user, the cloud server also needs to store tags uploaded by the verifier. To overcome this limitation, we propose a concrete construction of outsourced privately verifiable PoR (OPVPoR) without requiring the additional tag storage from the verifier. Furthermore, we extend the OPVPoR scheme to the multi-user setting and build a MOPVPoR scheme, where users storing the same data to the cloud server also share the tag information to further reduce the storage cost. Finally, we implement both schemes to evaluate their performance in practice.

Decentralized anonymous payment schemes may be exploited for illicit activities, such as money laundering, bribery and blackmail. To address this issue, several regulatory-friendly decentralized anonymous payment schemes have been proposed. However, most of these solutions lack restrictions on the regulator’s authority, which could potentially result in power abuse and privacy breaches. In this paper, we present a decentralized anonymous payment scheme with collaborative regulation (DAPCR). Unlike existing solutions, DAPCR reduces the risk of power abuse by distributing regulatory authority to two entities: Filter and Supervisor, neither of which can decode transactions to access transaction privacy without the assistance of the other one. Our scheme enjoys three major advantages over others: 1) Universality, achieved by using zk-SNARK to extend privacy-preserving transactions for regulation. 2) Collaborative regulation, attained by adding the ring signature with controllable linkability to the transaction. 3) Efficient aggregation of payment amounts, achieved through amount tags. As a key technology for realizing collaborative regulation in DAPCR, the ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature. The linker can extract pseudonyms from signatures and link signatures submitted by the same signer based on pseudonyms, without leaking the signer’s identity. The opener can recover the signer’s identity from a given pseudonym. The experimental results reflect the efficiency of DAPCR. The time overhead for transaction generation is 1231.2ms, representing an increase of less than 50% compared to ZETH. Additionally, the time overhead for transaction verification is only 1.2ms.

Multiview clustering algorithms have attracted intensive attention and achieved superior performance in various fields recently. Despite the great success of multiview clustering methods in realistic applications, we observe that most of them are difficult to apply to large-scale datasets due to their cubic complexity. Moreover, they usually use a two-stage scheme to obtain the discrete clustering labels, which inevitably causes a suboptimal solution. In light of this, an efficient and effective one-step multiview clustering (E2OMVC) method is proposed to directly obtain clustering indicators with a small-time burden. Specifically, according to the anchor graphs, the smaller similarity graph of each view is constructed, from which the low-dimensional latent features are generated to form the latent partition representation. By introducing a label discretization mechanism, the binary indicator matrix can be directly obtained from the unified partition representation which is formed by fusing all latent partition representations from different views. In addition, by coupling the fusion of all latent information and the clustering task into a joint framework, the two processes can help each other and obtain a better clustering result. Extensive experimental results demonstrate that the proposed method can achieve comparable or better performance than the state-of-the-art methods. The demo code of this work is publicly available at https://github.com/WangJun2023/EEOMVC.

With the rapid development of 5 G and 6 G technologies, vast amounts of data are being generated. To save costs and ensure data security, these data are typically uploaded to multiple cloud servers. For handling massive data, distributed storage is necessary. Additionally, remote data integrity checking is essential. To achieve this, the PDP paradigm (Provable Data Possession) has been proposed. Unfortunately, existing PDP schemes suffer from inefficiencies. The main cause is that the data must be divided into small blocks with a limited size, such as 160 bits for bilinear pairings-based PDP schemes. This approach incurs significant computation and communication costs.To address this issue, we propose a new model called synchronous blockchain-based DPDP (Distributed Provable Data Possession) with forward-security in multi-cloud storage. This new concept leverages blockchain, which is the foundation of cryptocurrency. The paper formalizes the system model and security model for the new concept. Furthermore, a concrete blockchain-based DPDP scheme is designed using blockchain and RSA. The proposed scheme is provably secure, and its performance is analyzed from both theoretical and implementation perspectives. Our analysis demonstrates that the proposed DPDP scheme is provably secure, synchronous, forward-secure, efficient, and practical.

With data being the essential factor of production in the new smart city, the volume of data has exploded. The management and sharing of massive data produced by various industries have become an urgent problem nowadays. Cloud technology facilitates the storage of vast volumes of data and the delivery of precise services upon the requests of users, making it a practical choice for data storage. Data owners can share their data in a flexible and open environment by storing it on a cloud platform. However, the sensitive nature of personal data underscores the importance of ensuring data security and privacy. Attribute-Based Encryption (ABE) is a viable approach to address the above concerns as it supports end-to-end security, one-to-many data sharing and fine-grained access control. In addition, to accommodate significant increase in complexity of data sharing environments and maximize the value of data, data owners will adjust the access policy in real-time based on feedback from data receivers and changes in data usability. The emergence of quantum computers poses significant security challenges to classical cryptographic difficult problems. We first propose a Ciphertext-Policy ABE scheme from lattices that supports access policy update and resists attacks including quantum, collusion, and chosen plaintext.

Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.

With the rapid development of various data acquisition technologies, more and more multimodal data come into being. It is important to integrate different modalities which are with high-dimensional features for boosting final multimodal data classification task. However, existing multimodal classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence during information fusion. In this paper, we propose a trustworthy multimodal classification network via multi-level confidence learning, referred to as MLCLNet. Considering that a large number of feature dimensions could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. In order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation and generate modal-specific initial classification labels. Since samples from different modalities should share consistent labels, a cross-modal label fusion module is deployed to capture the label correlations of different modalities. In addition, motivated the ideally orthogonality of final fused label matrix, we design a label confidence loss to supervise the network for learning more separable data representations. To the best of our knowledge, MLCLNet is the first work which integrates both feature and label-level confidence learning for multimodal classification. Extensive experiments on four multimodal medical datasets are conducted to validate superior performance of MLCLNet when compared to other state-of-the-art methods.

Blockchain has attracted more and more attention of academia, industry, and government in recent decades. Different usage demands have inspired various blockchain designs, forming different blockchain systems, which leads to information islands. Many cross-chain technologies have been proposed to link different blockchains together and expand the utility of blockchain. Nevertheless, the cross-chain technology is still in its infancy, which faces many problems that retard its wide application, for example, the issues related to security, privacy, and effectiveness. In order to further investigate cross-chain technologies, it is essential to understand its current state of arts. Although there are some surveys about cross-chain technologies driven by specific demands, the literature still lacks a comprehensive survey focusing on security, privacy, and effectiveness of cross-chain technologies. In this paper, we provide a review on existing cross-chain technologies based on a comprehensive set of criteria on security, privacy, and other performance. We first propose a blockchain interoperability architecture for the purpose of analyzing potential threats and problems regarding security, privacy, and effectiveness. We then summarize a set of criteria regarding these quality attributes. Next, we comprehensively review the representative works on cross-chain technologies according to a taxonomy based on applied types of techniques and cross-chain purposes. In each work review, we provide a serious discussion on its pros and cons by employing our proposed criteria. Finally, based on our review and analysis, we figure out a number of open issues and step ahead to direct future research directions on cross-chain technologies.

Intelligent service robots have become an indispensable aspect of modern‐day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human‐machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human‐machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human‐like dialogue experience.

Internet of Things (IoT) aims to create a vast network with billions of things that can seamlessly create and exchange data, establishing intelligent interactions between people and objects around them. It is characterized with openness, heterogeneity, and dynamicity, which inevitably introduce severe security, privacy, and trust issues that hinder the widespread application of IoT. Trust management (TM) holds great promise in identifying malicious nodes, maintaining trust relationships, and enhancing system security. Traditional TM systems (TMSs) can be classified into centralized, semi-centralized, and distributed ones, all three of which suffer from critical challenges and thus are not sufficient for facilitating IoT development. Blockchain, as a disruptive technology, can help addressing the challenges of TM in IoT, thanks to its advanced features, such as decentralization, consistency, and tamper-proofing. As a result, blockchain-based TM (BC-TM) has been extensively studied in recent years to achieve decentralized TM in IoT. However, it still lacks a comprehensive survey on the current state of the arts. To fill this gap, in this article, we conduct a serious survey on BC-TM in IoT. We first propose a set of evaluation criteria that should be met by a TMS in IoT. Then, we propose a taxonomy of TMSs and continue with a thorough review on BC-TM in IoT by employing the proposed criteria. In the end, based on the review, a series of open issues are identified, and future research directions are suggested.

Erasure coding has been extensively deployed in today's data centers to tackle prevalent failures, yet it is prone to substantial cross-rack traffic for parity updates. In this article, we propose a new rack-coordinated update mechanism to suppress the cross-rack update traffic, which comprises two successive phases: a delta-collecting phase that collects data delta chunks, and another selective parity update phase that renews the parity chunks based on the update pattern and parity layout. We further design${\sf RackCU}$, an optimal rack-coordinated update solution that achieves the theoretical lower bound of the cross-rack update traffic. We also perform reliability analysis, demonstrating that${\sf RackCU}$can attain a lower data loss probability via shortening the update procedure. We conduct extensive evaluations, in terms of large-scale simulation and real-world data center experiments. We show that${\sf RackCU}$can reduce 16.5-77.1% of the cross-rack update traffic and hence improve 24.9-772.0% of the update throughput.

Blockchain systems face two emergent problems, namely scalability and privacy, each of which has been addressed independently. However, how to achieve privacy and scalability simultaneously remains a challenging problem for blockchains. In this article, we propose a privacy-preserving and scalable permissioned blockchain system called HyperMaze employing the zero knowledge proof technique and a hierarchical system architecture. It gains scalability by adopting a hierarchy of multiple blockchains that processes transactions in parallel. We design anID-based dual-balance account modelwhere an identity-based account is associated with two types of balances–a plaintext balance and a private (zero-knowledge) balance. Furthermore, we design a two-phase cross-chain transaction mechanism (2PXT) to achieve transaction privacy for both intra-chain and cross-chain transactions. We rigorously formulate a security model for HyperMaze under the universal composability framework, and then provide a simulation-based security proof. A prototype of HyperMaze is implemented and a series of experiments are conducted over up to 2,600 nodes to evaluate its performance. The experimental results show that a 4-level, (7,10)-threshold, 6-ary HyperMaze system can reach 19,440 TPS and the transaction confirmation latency is only 9.5 seconds. To our best knowledge, HyperMaze is the first high-throughput privacy-preserving blockchain whose throughput is over 19,000 TPS.

Traditional data access control schemes only prevent unauthorized access to private data with a single owner. They are not suitable for application in a Multi-Level Data Processing (MLDP) scenario, where data are processed by a series of parties who also insert new data. Hence, the accumulated dataset should be protected through access control handled by hierarchically-structured parties who are at least partial data owners in MLDP. Existing multi-owner access control schemes mainly focus on controlling access to co-owned data of multiple entities with the equal ownership, but seldom investigates how to apply access control in MLDP. In this paper, we base the off-the-shelf Trusted Execution Environment (TEE), Intel SGX, to propose an Efficient and Secure Multi-owner Access Control scheme (ESMAC) for access authorization in MLDP. Moreover, to prevent unauthorized data disclosure by non-root data owners aiming to gain extra profits, we further introduce undercover polices to supervise their behaviors. Specifically, we design a data protection scheme based on game theory to decide the payoffs and punishments of honest and dishonest data owners, which motivates data owners to behave honestly when claiming ownership over data. Through comprehensive security analysis and performance evaluation, we demonstrate ESMAC's security and effectiveness.

Internet-of-Things enables interconnection of billions of devices, which perform autonomous operations and collect various types of data. These things, along with their generated huge amount of data, need to be handled efficiently and securely. Centralized solutions are not desired due to security concerns and scalability issue. In this article, we propose HIBEChain, a hierarchical blockchain system that realizes scalable and accountable management of IoT devices and data. HIBEChain consists of multiple permissioned blockchains that form a hierarchical tree structure. To support the hierarchical structure of HIBEChain, we design a decentralized hierarchical identity-based signature (DHIBS) scheme, which enables IoT devices to use their identities as public keys. Consequently, HIBEChain achieves high scalability through parallel processing as blockchain sharding schemes, and it also implements accountability by use of identity-based keys. Identity-based keys not only make HIBEChain more user-friendly, they also allow private key recovery by validators when necessary. We provide detailed analysis of its security and performance, and implement HIBEChain based on Ethereum source code. Experiment results show that a 6-ary, (7,10)-threshold, 4-level HIBEChain can achieve 32,000 TPS, and it needs only 9 seconds to confirm a transaction.

The emerging blockchain technology, combined with the smart contract paradigm, is expected to transform traditional applications with decentralization. When the blockchain technology is applied to decentralize traditional applications, blockchain validators may need to take in sensitiveoff-chaindata to execute a smart contract. On the one hand, decentralized applications (DApps) require authentic off-chain input data to correctly execute a given business procedure. On the other hand, users are reluctant to expose their sensitive privacy on the blockchain. For example, for a decentralized medical insurance DApp that takes as input personal health data, it is critical to guarantee authenticity and privacy of the data sent to the smart contract, such that the data can be verified by validators without leaking sensitive information. However, no satisfactory solution has been proposed to attain privacy and authenticity at the same time. In this work, we first present a highly efficient authenticated zero knowledge proof protocol called zk-DASNARK by extending the classical zk-SNARK scheme with data authentication. Based on zk-DASNARK, we design zk-AuthFeed, a zero-knowledge authenticated off-chain data feed scheme to achieve both data privacy and authenticity for blockchain-based DApps. Following the strategy of “compute off-chain and verify on-chain”, zk-AuthFeed can significantly reduce computation cost of blockchain validators. We fully implement a prototype of zk-AuthFeed, and conduct comprehensive experiments on a medical insurance DApp. We consider 4 typical computation models for insurance premium/reimbursement in the experiments. It shows that zk-AuthFeed is highly efficient: key generation takes about 10 seconds only, proof generation takes less than 4 seconds, and proof verification takes less than 40 ms.

Few-shot relation classification (FSRC) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRC tasks with similar categories that confuse the model to distinguish correctly. We argue this is largely due to two reasons, 1) ignoring pivotal and discriminate information that is crucial to distinguish confusing classes, and 2) training indiscriminately via randomly sampled tasks of varying difficulty. In this article, we introduce a novel prototypical network approach with contrastive learning that learns more informative and discriminative representations by exploiting relation label information. We further design two strategies that increase the difficulty of training tasks and allow the model to adaptively learn to focus on hard tasks. By doing so, our model can better represent subtle inter-relation variance and grow up through task difficulty. Extensive experiments on three standard benchmarks demonstrate the effectiveness of our method.

The Internet of Vehicles (IoV) technology enables vehicles to communicate with each other, with pedestrians and with roadside infrastructures, to realize more efficient, safer and more environmentally friendly transportation. IoV also promises rich location-based services for vehicles, such as parking and toll highway. However, preserving privacy for location-based service payments emerges as a critical and challenging problem in IoV. Existing schemes rely on centralized banks for payment processing, resulting in location privacy leakage to centralized entities. In this paper, we propose a decentralized privacy-preserving payment scheme named Vehicloak for IoV based on the blockchain technology. The biggest challenge is to provide location privacy for vehicles while guaranteeing correct service payments using the transparent blockchain. To tackle this challenge, we introduce a new cryptographic technique called zk-GSigproof that integrates zero-knowledge proof with group signature. Vehicloak implements this technique in a smart contract to process payment, which verifies zero-knowledge proof and group signature without leaking location information. It is not limited to IoV and can be applied in many payment scenarios. To evaluate the performance of our scheme, we implement Vehicloak on a private blockchain of 100 nodes on Aliyun, and conduct a test with up to 4,000 transactions. The experimental results prove the feasibility of Vehicloak.

In recent years, the identification of out-of-distribution faults has become a hot topic in the field of intelligent diagnosis. Existing researches usually adopt domain adaptation methods to complete the generalization of diagnostic knowledge with the aid of target domain data, but the acquisition of fault samples in real industries is extremely time-consuming and costly. Moreover, most researches focus on samples with fixed fault levels, ignoring the fact that system degradation is a continuous process. In response to the above intractable problems, this article proposed a causal disentanglement network (CDN) to realize cross-machine knowledge generalization and continuous degradation mode diagnosis. In CDN, multitask instance normalization and batch normalization structure was proposed to learn task-specific knowledge and enhance the informativeness of the extracted features. On this basis, a causal disentanglement loss was proposed, which minimized the mutual information of features between subtask structures and captured the causal invariant fault information for better generalization. The experimental results proved the superiority and generalization ability of CDN, and the visualization results proved the performance of CDN in causality mining.

Security is critical to networks, but TCP/IP-basedlegacynetworks are difficult to advance new security functions due to the use of costly inflexible hardware devices and error-prone network configurations. Recent literature explores the paradigm of consolidating security services with the forwarding functionality using Software-defined Networking (SDN). Existingfull SDNdeployment, replacing all legacy network devices with SDN devices, is cost-prohibitive. Whereas thehybrid SDNthat only upgrades partial legacy devices to SDN switches is considered practical. However, the challenge is to minimize threats and deployment expenses simultaneously under heterogeneous end-host businesses that have various importance. In this paper, we study the challenge and propose theEnd-host-importance-Aware secure service-enabled hybrid Sdn deplOymeNt (EASON)problem. We mathematically formulate the EASON problem as an integer programming problem, prove its non-polynomial time complexity, and propose a heuristic algorithm called BonSèc. We conduct rigorous simulations on real-world topologies and traces. Experimental results show that BonSèc achieves comparable security and cost performances to the optimal solution on small topologies. Meanwhile, it is scalable on larger topologies.

Multi-organization data sharing is becoming increasingly prevalent due to the interconnectivity of systems and the need for collaboration across organizations (e.g., exchange of data in a supply chain involving multiple upstream and downstream vendors). There are, however, data security concerns due to lack of trust between organizations that may be located in jurisdictions with varying security and privacy legislation and culture (also referred to as a zero trust environment). Hence, in such a zero trust setting, one should introduce strengthened, yet efficient, access control mechanisms to facilitate cross-organizational data access and exchange requests. Contemporary access control schemes generally focus on protecting a single objective rather than multiple parties, due to higher security costs. In this article, we propose a blockchain-based access control scheme, designed to facilitate lightweight data sharing among different organizations. Specifically, our approach utilizes the consortium blockchain to establish a trustworthy environment, in which a Role-Based Access Control (RBAC) model is then deployed using our proposed multi-signature protocol and smart contract methods. Evaluation of our proposed approach is performed on the HyperLedger Fabric consortium blockchain platform using both Caliper and BFT-SMaRT benchmarks, and the findings demonstrate the utility of our approach.

Fibonacci and Galois are two different kinds of configurations in stream ciphers. Although many transformations between two configurations have been proposed, there is no sufficient analysis of their FPGA performance. Espresso stream cipher provides an ideal sample to explore such a problem. The 128-bit secret key Espresso is designed in Galois configuration, and there is a Fibonacci-configured Espresso variant proved with the equivalent security level. To fully leverage the efficiency of two configurations, we explore the hardware optimization approaches toward area and throughput, respectively. In short, the FPGA-implemented Fibonacci cipher is more suitable for extremely resource-constrained or high-throughput applications, while the Galois cipher compromises both area and speed. To the best of our knowledge, this is the first work to systematically compare the FPGA performance of cipher configurations under relatively fair cryptographic security. We hope this work can serve as a reference for the cryptography hardware architecture research community.

Fruit-80, which emerged as an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource-constrained devices in the Internet of Things. In this article, we propose area and speed optimization architectures of Fruit-80 on FPGAs. Our implementations include both serial and parallel structure and optimize area, power, speed, and throughput, respectively. The area optimization architecture aims to achieve the most suitable ratio of look-up-tables and flip-flops to fully utilize the reconfigurable unit. It also reuses NFSR and LFSR feedback functions to save resources for high throughput. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. Besides, we recommend using the round key function to optimize serial or parallel implementations for Fruit-80 and using indexing and shifting methods for different throughput. In conclusion, our results show that the area optimization architecture occupies up to 35 slices on Xilinx Spartan-3 FPGA and 18 slices on Xilinx 7 series FPGA, smaller than that of Grain and other common stream ciphers. The optimal throughput/area ratio of the speed optimization architecture is 7.74 Mbps/slice, better than that of Grain v1, which is 5.98 Mbps/slice. The serial implementation of Fruit-80 with round key function occupies only 75 slices on Spartan-3 FPGA. To the best of our knowledge, the result sets a new record of the minimum area in lightweight cipher implementation on FPGA.

It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This article presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.

Fruit-80, an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource constrained devices in the Internet of Things. In this paper, we propose area and speed optimization architectures of Fruit-80 on FPGAs. The area optimization architecture reuses NFSR&LFSR feedback functions and achieves the most suitable ratio of look-up-tables and flip-flops. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. In conclusion, the optimal throughput-to-area ratio of the speed optimization architecture is better than that of Grain v1. The area optimization architecture occupies only 35 slices on Xilinx Spartan-3 FPGA, smaller than that of Grain and other common stream ciphers. To the best of our knowledge, this result sets a new record of the minimum area in lightweight cipher implementations on FPGA.

Cloud computing offers resource-constrained users big-volume data storage and energy-consuming complicated computation. However, owing to the lack of full trust in the cloud, the cloud users prefer privacy-preserving data computation with correctness verification. However, cryptography-based schemes introduce high computational costs to both the cloud and its users for verifiable computation with privacy preservation, which makes it difficult to support complicated computations in practice. Intel Software Guard Extensions (SGX) as a trusted execution environment is widely researched in various fields, and is regarded as a promising way to achieve efficient outsourced data computation with privacy preservation over the cloud. But we find two types of threats towards the computation with SGX: Disarranging Data-Related Code threat and Output Tampering and Misrouting threat. In this paper, we depict these threats using formal methods and propose an efficient and secure scheme to resist the threats and realize verifiable computation for Intel SGX. We prove the security and show the efficiency and correctness of our proposed scheme through theoretic analysis and extensive experiments. Furthermore, we compare our scheme with some cryptography-based schemes to show its high efficiency.

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided. This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions.

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

The disruptive blockchain technology is expected to have broad applications in many areas due to its advantages of transparency, fault tolerance, and decentralization, but the open nature of blockchain also introduces severe privacy issues. Since anyone can deduce private information about relevant accounts, different privacy-preserving techniques have been proposed for cryptocurrencies under the UTXO model, e.g., Zerocash and Monero. However, it is more challenging to protect privacy for account-model blockchains (e.g., Ethereum) since it is much easier to link accounts in the account-model blockchain. In this article, we proposeBlockMaze, an efficient privacy-preserving account-model blockchain based on zk-SNARKs. Along with dual-balance model, BlockMaze achieves strong privacy guarantees by hiding account balances, transaction amounts, and linkage between senders and recipients. Moreover, we provide formal security definitions and prove the security ofBlockMaze. Finally, we implement a prototype ofBlockMazebased on Libsnark and Go-Ethereum, and conduct extensive experiments to evaluate its performance. Our 300-node experiment results show that BlockMaze has high efficiency in computation and transaction throughput: one transaction verification takes about 14.2 ms, one transaction generation takes 6.1-18.6 seconds, and its throughput is around 20 TPS.

With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two-way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this article, we propose a privacy-preserving fair exchange schemeV2GExfor V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions costs only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.

Blockchain has been utilized to manage distributed multicloud storage in the industrial Internet of Things. Existing approaches commonly use trusted third-party servers or middlewares to search data allocation strategies and use blockchain to enhance security. However, finding a fair data allocation strategy is hard when the third-party brokers are manipulated. Moreover, the complex computing in generating blocks reduces efficiency and heavy communication cost in consensus leads to critical challenges to scalability. To address that, this article proposes a scalable two-layer blockchain system for distributed multi-cloud storage (STSM). We design a novel consensus mechanism called proof of storage allocation, which integrates data placement problems into leader selection to achieve fair strategy and high QoS of data storage. We also incorporate asynchronous consensus groups into the consensus process to enhance scalability. Extensive experiments verify that STSM gains high scalability and increases efficiency while achieving high QoS in distributed multicloud data allocation.

Sensors in wireless sensor network (WSN) are usually deployed in the wild or even hostile circumstance. What is worse, most of the sensors have limited communication bandwidth, computation resources and energy. Therefore, it is challenging to ensure the security of WSN without decreasing its network performance. Network coding (NC) is a promising way for improving communication capability in WSN, e.g., high throughput, robustness and low-energy. Nevertheless, network coding is vulnerable to malicious attacks. Presently, many secure detections, such as information theoretic-based or cryptographic-based techniques, have been proposed to deal with a single type of attack, but are incapable of resisting the joint attacks, e.g., the union of pollution attacks and replay attacks. In this paper, a secure detection service is presented. It is deployed on every node of WSN to monitor, manage and control the messages passing through them in real-time. In the service, a lightweight timestamp-based message authentication code, namely TMAC, is designed with Exclusive OR network coding. Based on TMAC and time synchronization technique, a joint detection is implemented to resist pollution attacks and replay attacks synchronously. The correctness of the detection service is proved. Finally, the performance evaluation shows that the detection scheme brings negligible extra-expense in communication bandwidth and computational complexity compared to MAC-based schemes, and consumes energy lowly compared to other joint detection schemes.

Blockchain-based artificial intelligence (BC-AI) has been applied for protecting deep neural network (DNN) data from being tampered with, which is expected to further boost trusted distributed AI applications in many fields. However, due to smart contract execution environment architectural defects, it is challenging for previous BC-AI systems to support computing-intensive tasks on-chain performing such as DNN convolution operations. They have to offload computations and a large amount of data from blockchain to off-chain platforms to execute smart contracts as native code. This failure to take advantage of data locality has become one of the major critical performance bottlenecks in BC-AI system. To this end, in this article, we propose SmartVM with optimization methods to support on-chain DNN inference for BC-AI system. The key idea is to design and optimize the computing mechanism and storage structure of smart contract execution environment according to the characteristics of DNN such as high computational parallelism and large data volume. We decompose SmartVM into three components: 1) a compact DNN-oriented instruction set to describe computations in a short number of instructions to reduce interpretation time. 2) a memory management mechanism to make SmartVM memory dynamic free/allocated according to the size of DNN feature maps. 3) a block-based weight prefetching and parallel computing method to organize each layer's computing and weights prefetching in a pipelined manner. We perform the typical image classification in a private Ethereum blockchain testbed to evaluate SmartVM performance. Experimental results highlight that SmartVM can support DNN inference on-chain with roughly the same efficiency against the native code execution. Compared with the traditional off-chain computing, SmartVM can speed up the overall execution by70×,16×,11×, and12×over LeNet5, AlexNet, ResNet18, and MobileNet, respectively. The memory footprint can be reduced by84%,90.8%,94.3%, and93.7%over the above four models, while offering the same level model accuracy. This article sheds light on the design space of the smart contract virtual machine for DNN computation and is promising to further boost BC-AI applications.

Compared with traditional insurance schemes, usage-based insurance (UBI) for vehicles is more economic and accurate for drivers since its insurance premium calculation depends on how vehicles are driven. However, UBI requires sensitive driving data to determine insurance premiums, and this could result in serious privacy breach for drivers. Meanwhile, existing UBI solutions rely on a centralized entity (i.e., the insurance company) to manage insurances. In this article, we design a decentralized and privacy-preserving UBI scheme, called DUBI, based on the blockchain technology and zero-knowledge proof. In our scheme, a smart contract running over the blockchain serves as a “decentralized” insurance company, while drivers continuously upload their committed driving data to the blockchain. Periodically, the driver submits accumulated driving statistics with a zero-knowledge proof to the smart contract, which verifies the proof and calculates the insurance premium from the submitted statistics. We formulate an ideal functionality for DUBI under the universal composability framework, and then provide a formal security proof for DUBI. Furthermore, we give in-depth analysis and performance evaluation for DUBI with an implementation based on Ethereum. It shows that DUBI is highly efficient in processing UBI insurances in both storage and computation: DUBI is about seven times more efficient than existing schemes in storage, and proof generation and verification take only 7 and 30 ms, respectively.

Election and voting play crucial roles in democratic society for an elactorate to make a collective decision. E-voting is one of the most challenging problems in cryptographic research to provide multiple dimensions security assurances. In this paper, we study an important voting paradigm, score voting, with privacy protection, which has not been investigated in previous work. We propose a blockchain based self-tallying election system to support score voting, dubbed “PriScore”, where the ballots are recorded on blockchain to prevent vote forgery or tampering. PriScore makes it possible for each voter to assign different evaluation scores (within a certain range) for the candidates as ranked-choice, where the sum of the scores in each ballot should be a predefined constant, and the evaluation scores are encrypted to maintain confidentiality. A major challenge in score voting is to simultaneously prove two constraint conditions: range proof and sum proof. We introduce a new technique, called dual zero-knowledge proof (dual-ZKP), to prove the scores satisfying two crucial requirements, which integrates “1-out-of-$K$” proof and distributed ElGamal crypto in a non-trivial way. The self-tallying mechanism in PriScore enables any party in the system to calculate and verify the election result, which provides fairness, dispute-freeness. The security analysis demonstrates that PriScore achieves completeness, soundness, eligibility, universal/individual verifiability and multiple-voting detection. We evaluate the performance of PriScore on modern workbench to test the performance, and also on a blockchain platform to measure the resource consumption. The experiments show that PriScore preserves privacy of score voting with reasonable overheads.

The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group G to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.

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

Smart home IoT devices have been more prevalent than ever before but the relevant security considerations fail to keep up with due to device and technology heterogeneity and resource constraints, making IoT systems susceptible to various attacks. In this paper, we propose a novel graph-based mechanism to identify the vulnerabilities in communication of IoT devices for smart home systems. Our approach takes one or more packet capture files as inputs to construct a traffic graph by passing the captured messages, identify the correlated subgraphs by examining the attribute-value pairs associated with each message, and then quantify their vulnerabilities based on the sensitivity levels of different keywords. To test the effectiveness of our approach, we setup a smart home system that can control a smart bulb LB100 via either the smartphone APP for LB100 or the Google Home speaker. We collected and analyzed 58,714 messages and exploited 6 vulnerable correlated sub graphs, based on which we implemented 6 attack cases that can be easily reproduced by attackers with little knowledge of IoT. This study is novel as our approach takes only the collected traffic files as inputs without requiring the knowledge of the device firmware while being able to identify new vulnerabilities. With this approach, we won the third prize out of 20 teams in a hacking competition.

Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plaintexts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient since they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and achieves significant performance improvement over existing schemes. We formally define its security, and prove that it is selectively secure in the standard model. Also, we implement the proposed scheme using a rapid prototyping tool called Charm [37], and conduct several experiments to evaluate it performance. The results demonstrate that our scheme is much more efficient than the ones built over the composite-order groups.

Although cloud computing offers elastic computation and storage resources, it poses challenges on verifiability of computations and data privacy. In this work we investigate verifiability for privacy-preserving multi-keyword search over outsourced documents. As the cloud server may return incorrect results due to system faults or incentive to reduce computation cost, it is critical to offer verifiability of search results and privacy protection for outsourced data at the same time. To fulfill these requirements, we design aVerifiablePrivacy-preserving keywordSearch scheme, called VPSearch, by integrating an adapted homomorphic MAC technique with a privacy-preserving multi-keyword search scheme. The proposed scheme enables the client to verify search results efficiently without storing a local copy of the outsourced data. We also propose a random challenge technique with ordering for verifying top-$k$search results, which can detect incorrect top-$k$results with probability close to 1. We provide detailed analysis on security, verifiability, privacy, and efficiency of the proposed scheme. Finally, we implement VPSearch using Matlab and evaluate its performance over three UCI bag-of-words data sets. Experiment results show that authentication tag generation incurs about 3 percent overhead only and a search query over 300,000 documents takes about 0.98 seconds on a laptop. To verify 300,000 similarity scores for one query, VPSearch costs only 0.29 seconds.

We propose two ciphertext-policy attribute-based key encapsulation mechanism (CP-AB-KEM) schemes that for the first time achieve both outsourced encryption and outsourced decryption in two system storage models and give corresponding security analysis. In our schemes, heavy computations are outsourced to Encryption Service Providers (ESPs) or Decryption Service Providers (DSPs), leaving only one modular exponentiation computation for the sender or the receiver. Moreover, we propose a general verification mechanism for a wide class of ciphertext-policy (cf. key-policy) AB-KEM schemes, which can check the correctness of the outsourced encryption and decryption efficiently. Concretely, we introduce a stronger version of verifiability (cf.  [1] ) and a new security notion for outsourced decryption called exculpability, which guarantees that a user cannot accuse DSP of returning incorrect results while it is not the case. With all these mechanisms, any dispute between a user and an outsource computation service provider can be easily resolved, furthermore, a service provider will be less motivated to give out wrong results. Finally, we implement our schemes in Charm  [2] , and the results indicate that the proposed schemes/mechanisms are efficient and practical.

Ma et al. recently submitted a comment correspondence which points out a flaw in our paper (a sequel of our earlier paper published in the Proceedings of IEEE INFOCOM). The flaw led to the leakage of the system-wide master key; therefore, we improved our own scheme by addressing it.

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been proposed to secure the cloud storage. However, most work focuses on the data contents privacy and the access control, while less attention is paid to the privilege control and the identity privacy. In this paper, we present a semianonymous privilege control scheme AnonyControl to address not only the data privacy, but also the user identity privacy in existing access control schemes. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semianonymity. Besides, it also generalizes the file access control to the privilege control, by which privileges of all operations on the cloud data can be managed in a fine-grained manner. Subsequently, we present the AnonyControl-F, which fully prevents the identity leakage and achieve the full anonymity. Our security analysis shows that both AnonyControl and AnonyControl-F are secure under the decisional bilinear Diffie-Hellman assumption, and our performance evaluation exhibits the feasibility of our schemes.

Maximum multiflow and maximum concurrent mul-tiflow in multi-channel multi-radio (MC-MR) wireless networks have been well-studied in the literature. They are NP-hard even in single-channel single-radio (SC-SR) wireless networks when all nodes have uniform (and fixed) interference radii and the positions of all nodes are available. While they admit a polynomial-time approximation scheme (PTAS) when the number of channels is bounded by a constant, such PTAS is quite infeasible practically. Other than the PTAS, all other known approximation algorithms, in both SC-SR wireless networks and MC-MR wireless networks, resorted to solve a polynomial-sized linear program (LP) exactly. The scalability of their running time is fundamentally limited by the general-purposed LP solvers. In this paper, we first introduce the concept of interference costs and prices of a path and explore their relations with the maximum (concurrent) multiflow. Then we develop purely combinatorial approximation algorithms which compute a sequence of least interference-cost routing paths along which the flows are routed. These algorithms are faster and simpler, and achieve nearly the same approximation bounds known in the literature.

Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. Those advantages, ironically, are the causes of security and privacy problems, which emerge because the data owned by different users are stored in some cloud servers instead of under their own control. To deal with security problems, various schemes based on the Attribute-Based Encryption have been proposed recently. However, the privacy problem of cloud computing is yet to be solved. This paper presents an anonymous privilege control scheme AnonyControl to address not only the data privacy problem in a cloud storage, but also the user identity privacy issues in existing access control schemes. By using multiple authorities in cloud computing system, our proposed scheme achieves anonymous cloud data access and fine-grained privilege control. Our security proof and performance analysis shows that AnonyControl is both secure and efficient for cloud computing environment.

For wireless link scheduling in multi-channel multi-radio wireless networks aiming at maximizing (concurrent) multi-flow, constant-approximation algorithms have recently been developed in [11]. However, the running time of those algorithms grows quickly with the number of radios per node (at least in the sixth order) and the number of channels (at least in the cubic order). Such poor scalability stems intrinsically from the exploding size of the fine-grained network representation upon which those algorithms are built. In this paper, we introduce a new structure, termed as concise conflict graph, on the node-level links directly. Such structure succinctly captures the essential advantage of multiple radios and multiple channels. By exploring and exploiting the rich structural properties of the concise conflict graphs, we are able to develop fast and scalable link scheduling algorithms for either minimizing the communication latency or maximizing the (concurrent) multi-flow. These algorithms have running time growing linearly in both the number of radios per node and the number of channels, while not sacrificing the approximation bounds.

Static greedy link schedulings have much simpler implementation than dynamic greedy link schedulings such as Longest-queue-first (LQF) link scheduling. However, its stability performance in multi-channel multi-radio (MC-MR) wireless networks is largely under-explored. In this paper, we present a stability subregion with closed form of a static greedy link scheduling in MC-MR wireless networks under the 802.11 interference model. By adopting some special static link orderings, the stability subregion is within a constant factor of the stable capacity region of the network. We also obtain constant lower bounds on the throughput efficiency ratios of the static greedy link schedulings in some special static link orderings.

As the advancement of sensing and networking technologies, participatory sensing has raised more and more attention as it provides a promising way enabling public and professional users to gather and analyze private data to understand the world. However, in these participatory sensing applications both data at the individuals and analysis results obtained at the users are usually private and sensitive to be disclosed, e.g., locations, salaries, utility usage, consumptions, behaviors, etc. A natural question, also an important but challenging problem is how to keep both participants and users data privacy while still producing the best analysis to explain a phenomenon. In this paper, we have addressed this issue and proposed M-PERM, a mutual privacy preserving regression modeling approach. Particularly, we launch a series of data transformation and aggregation operations at the participatory nodes, the clusters, and the user. During regression model fitting, we provide a new way for model fitting without any need of the original private data or the exact knowledge of the model expression. To evaluate our approach, we conduct both theoretical analysis and simulation study. The evaluation results show that the proposed approach produces exactly the same best model as if the original private data were used without leakage of the fitted model to any participatory nodes, which is a significant advance compared with the existing approaches [1-5]. It is also shown that the data gathering design is able to reach maximum privacy protection under certain conditions and be robust against collusion attack. Furthermore, compared with existing works under the same context (e.g., [1-5]), to our best knowledge it is the first work showing that not only the model coefficients estimation but also a series of regression analysis and model selection methods are reachable in mutual privacy preserving data analysis scenarios such as participatory sensing.

Pay-TV broadcasting system is an extensively deployed application that charges users based on their subscription. To ensure security for the Pay-TV broadcasting application, a conditional access system (CAS) is designed to control TV channel/program access to only the authorized subscribers. Several key management schemes with a four-level hierarchical key structure have been proposed. In this paper, we point out a severe security weakness of these schemes against collusion attacks. Then we propose a new CAS scheme with a three-level hierarchical key structure using ciphertext-policy attribute-set-based encryption (ASBE), an extension of ciphertext-policy attribute-based encryption (CP-ABE). Our scheme achieves scalable, flexible, fine-grained, and most importantly, collusion-resistant access control for Pay-TV broadcasting applications. The proposed scheme is designed to support all operations in Pay-TV applications. We then provide a detailed analysis on security and performance of our scheme. We also implement the scheme and it is showed to be both efficient and flexible for Pay-TV broadcasting applications.

Traffic analysis presents a serious threat to wireless network privacy due to the open nature of wireless medium. Traditional solutions are mainly based on the mix mechanism proposed by David Chaum, but the main drawback is its low network performance due to mixing and cryptographic operations. We propose a novel privacy preserving scheme based on network coding called Priv-Code to counter against traffic analysis attacks for wireless communications. Priv-Code is able to provide strong privacy protection for wireless networks as the mix system because of its intrinsic mixing feature, and moreover, it can achieve better network performance owing to the advantage of network coding. We first construct a hypergraph-based network coding model for wireless networks, under which we formalize an optimization problem whose objective function is to make each node have identical transmission rate. Then we provide a decentralized algorithm for this optimization problem. After that we develop an information theoretic metric for privacy measurement using entropy, and based on this metric we demonstrate that Priv-Code achieves stronger privacy protection than the mix system while achieving better network performance.

Longest-queue-first (LQF) link scheduling is a greedy link scheduling in multihop wireless networks. Its stability performance in single-channel single-radio (SC-SR) wireless networks has been well studied recently. However, its stability performance in multi-channel multi-radio (MC-MR)wireless networks is largely under-explored. In this paper, we present a stability subregion with closed form of the LQF scheduling in MC-MR wireless networks, which is within a constant factor of the network stability region. We also obtain constant lower bounds on the efficiency ratio of the LQF scheduling in MC-MR wireless networks under the 802.11 interference model or the protocol interference model.

Cloud computing has emerged as one of the most influential paradigms in the IT industry in recent years. Since this new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing; however, most of them suffer from inflexibility in implementing complex access control policies. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending ciphertext-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. In addition, HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. We formally prove the security of HASBE based on security of the ciphertext-policy attribute-based encryption (CP-ABE) scheme by Bethencourtand analyze its performance and computational complexity. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments.

Privacy-preserving routing is crucial for some ad hoc networks that require stronger privacy protection. A number of schemes have been proposed to protect privacy in ad hoc networks. However, none of these schemes offer complete unlinkability or unobservability property since data packets and control packets are still linkable and distinguishable in these schemes. In this paper, we define stronger privacy requirements regarding privacy-preserving routing in mobile ad hoc networks. Then we propose an unobservable secure routing scheme USOR to offer complete unlinkability and content unobservability for all types of packets. USOR is efficient as it uses a novel combination of group signature and ID-based encryption for route discovery. Security analysis demonstrates that USOR can well protect user privacy against both inside and outside attackers. We implement USOR on ns2, and evaluate its performance by comparing with AODV and MASK. The simulation results show that USOR not only has satisfactory performance compared to AODV, but also achieves stronger privacy protection than existing schemes like MASK.

Online social network data are increasingly made publicly available to third parties. Recent studies show that it is possible to recover sensitive information from the released data and several anonymization techniques have been proposed to protect individual privacy. However, most of the existing defenses have focused on ``one-time'' releases and do not take into consideration the re- publication of dynamic social network data. Re- publishing data periodically is a natural result of social network evolution and an emerging requirement of dynamic social network analysis. In this paper, we show that by utilizing correlations between sequential releases, the adversary can achieve high precision in de-anonymization of the released data, suppressing the uncertainty of re-identifying each release separately and synthesizing the results afterwards. Besides, we combine structural knowledge with node attributes to compromise graph modification based defenses. With experiments on real data, this work is the first to demonstrate feasibility of de-anonymizing dynamic social networks and should arouse concern for future works on privacy preservation in social network data publishing.

As a combination of ad hoc networks and wireless local area network (WLAN), the wireless mesh network (WMN) provides a low-cost convenient solution to the last-mile network-connectivity problem. As such, existing route protocols designed to provide security and privacy protection for ad hoc networks are no longer applicable in WMNs. On the other hand, little research has focused on privacy-preserving routing for WMNs. In this paper, we propose two solutions for security and privacy protection in WMNs. The first scheme relies on group signatures, together with user credentials, to deliver security and privacy protection. By enforcing access control using user credentials, the user's identity has to be disclosed to mesh routers. To avoid this, our second scheme employs pairwise secrets between any two users to achieve stronger privacy protection. In the second scheme, the user is kept anonymous to mesh routers. Finally, we analyze these two schemes in terms of security, privacy, and performance.

Location privacy in wireless sensor networks has gained a wide concern. Particularly, the location privacy of base station requires ultimate protection due to its crucial position in wireless sensor networks. In this paper, we propose an efficient scheme, consisting of anonymous topology discovery and intelligent fake packet injection (IFPI), to protect the location privacy of base station. Anonymous topology discovery eliminates the potential threats against base station within topology discovery period. On the other hand, IFPI enhances privacy protection strength during data transmission period. Under given conditions, comprehensive simulations demonstrate that our scheme significantly improves privacy strength compared with existing strategies.

A key challenge of the resource-constrained wireless sensor network is to prolong the lifetime as long as possible. Researchers have proved data transmission consumes most energy of sensor nodes, and the routing policy has great influence on network lifetime. Another important factor is the location confidentiality of base station that may be easily captured by a packet-tracing adversary due to the open communication nature of sensor network. In this paper, we design a new routing scheme called CLEAR: A Confidential and Lifetime-Aware Routing Protocol. In CLEAR, the paths between sources and base station keep changing throughout the whole lifetime in order to make best use of the limited node energy. On the other hand, we introduce an extended confidentiality protection mechanism called Branching, combining with the basic confidentiality feature of CLEAR as a whole. Comprehensive simulations prove that CLEAR extends the lifetime of sensor network almost twice as much as that of the basic routing protocol, and prominently enhances the base station location confidentiality compared with several existing schemes.

Popularity of group-oriented applications motivates research on security and privacy protection for group communications. A number of group key agreement protocols exploiting ID-based cryptosystem have been proposed for this objective. Though bearing beneficial features like reduced management cost, private key delegation from ID-based cryptosystem, they have not taken into account privacy issues during group communication. In wireless networks, the privacy problem becomes more crucial and urgent for mobile users due to the open nature of radio media. In this paper, we proposed an anonymous ID- based group key agreement protocol for wireless networks. Based on ID-based cryptosystem, our protocol not only benefits from the desirable features of ID-based cryptosystem, but also provides privacy protection for mobile users. More important, in the proposed protocol, the computation cost for each group member is largely reduced to meet the computation capability restriction of mobile devices.

Roaming services in wireless networks provide people with preferable flexibility and convenience. However, such ad-vantages should be offered with both security and privacy in mind. With consideration on privacy protection during roaming in wireless networks, we proposed a hierarchical ID-based roaming protocol in this paper. In our scheme, we use a 2-layer hierarchical ID-based cryptosystem in which a trusted party acts as the root authority, each domain server acts as the second-layer authority, and the roaming user is the end user. With the hierarchical ID-based cryptosystem, we can avoid involvement with home network, and keep the roaming the user’s identity private. Furthermore, not only the root authority is relieved from management of a large amount of private/public key pairs, but the domain servers are free to generate key pairs for their registered users. At the same time, we use hash chains together with ID-based signatures to achieve non-repudiation for service payment. Categories and Subject Descriptors

In this paper, we propose a highly efficient key agreement scheme based on a novel key tree construction for ad hoc networks. The key tree is constructed taking into consideration of the multicast tree which represents the underlying network topology. Our scheme greatly reduces the communications and computation cost for group key agreement and has high flexibility in having dynamic group memberships. We implement our scheme in ns-2 and evaluate its performance in terms of overhead and communication cost. The simulation results show that our scheme enjoys great advantages over other schemes in the literature

The conference key distribution scheme (CKDS) enables three or more parties to derive a common conference key to protect the conversation content in their conference. Designing a conference key distribution scheme for mobile communications is a difficult task because wireless networks are more susceptible to attacks and mobile devices usually obtain low power and limited computing capability. In this paper we study a conference scheme for mobile communications and find that the scheme is insecure against the replay attack. With our replay attack, an attacker with a compromised conference key can cause the conferees to reuse the compromised conference key, which in turn completely reveals subsequent conversation content.

Most of previous research work in key management can only resist passive attacks, such as dropping the certificate request, and are vulnerable under active attacks, such as returning a fake reply to the node requesting the certification service. In this paper, we propose two algorithms to address both security and efficiency issues of certification services in ad hoc networks. Both of the algorithms can resist active attacks. In addition, simulation results show that, compared to the previous works, our second algorithm is not only much faster in a friendly environment, but it also works well in a hostile environment in which existing schemes work poorly. Furthermore, the process of generating partial certificates in our second algorithm is extremely fast. Such advantage is critical in ad hoc networks where by nature the less help a node requests from its neighbors, the higher is the chance of obtaining the help. Consequently, using our second algorithm, a node can easily find enough neighboring nodes which provide the certification service.

Wireless networks have gained overwhelming popularity over their wired counterpart due to their great flexibility and convenience, but access control of wireless networks has been a serious problem because of the open medium. Passwords remain the most popular way for access control as well as authentication and key exchange. But existing password-based access control protocols are not satisfactory in that they do not provide DoS-resistance or anonymity. In this paper we analyze the weaknesses of an access control protocol using passwords for wireless networks in IEEE LCN 2001, and propose a different access control protocol using passwords for wireless networks. Our new protocol avoids the weaknesses of the previous protocol, and the client can anonymously authenticate himself to the server with a human-memorable password, while the server is free of DoS attacks. We also present detailed security and performance analysis for our protocols, and show that our protocol is both secure and efficient for access control in wireless networks

In this paper, we review a PKC (public key cryptosystem) based protocol, referred to as the Stanford protocol, aimed at overcoming several security deficiencies in IEEE 802.1X and to provide access control in both wireless and wired networks. One main objective of the Stanford protocol is to provide DoS resistance for the wireless network. Meanwhile, in the wireless environment, identity confidentiality of the mobile user is especially important since the disclosed identity could be used to locate the user and track his movement. But our analysis shows that the Stanford protocol fails to fulfill these requirements. So we propose a new PKC-based protocol that not only provides DoS resistance and perfect forward secrecy, but also provides identity anonymity for the clients. We also present detailed security and performance analysis for our protocol, and show that our protocol is secure and efficient for access control in wireless networks.

Although there are a large number of papers on secure routing in mobile ad-hoc networks, only a few consider the anonymity issue. We define more strict requirements on the anonymity and security properties of the routing protocol, and notice that previous research works only provide weak location privacy and route anonymity, and are vulnerable to specific attacks. Therefore, we propose the anonymous secure routing (ASR) protocol that can provide additional properties on anonymity, i.e. identity anonymity and strong location privacy, and at the same time ensure the security of discovered routes against various passive and active attacks. Detailed analysis shows that ASR can achieve both anonymity and security properties, as defined in the requirements, of the routing protocol in mobile ad-hoc networks.