A straightforward method for multimodal palm-based authentication is to integrate palm shape into the system, which enhances reliability, security, and accuracy compared to unimodal methods. However, most existing methods rely on handcrafted feature extraction, which fails to fully exploit palm shape information. Moreover, there have been limited attempts to apply deep learning-based methods in this field. This paper explores a deep multimodal fusion method of palm vein (PV) and palm shape (PS) for authentication called multimodal contrastive learning channel-exchanging networks (MCCENet) to better utilize palm shape contour information. Specifically, we observe that the discriminative palm shape contour information is primarily captured in the shallow layers of the model, while the deeper layers tend to focus on irrelevant local high-level semantics. Based on this, we design hierarchical feature fusion (HFF), a module that enables inter-modal channel exchange at shallow layers. Further, we introduce a multimodal contrastive learning loss to align features across modalities, enhancing their representational embeddings. Extensive experiments across eight widely-used public datasets demonstrate that MCCENet achieves state-of-the-art performance in all cases.

Most existing range Searchable Symmetric Encryption (SSE) schemes are static, making them vulnerable to volume pattern leakage attacks. While Volume-Hiding Range SSE (VH-RSSE) was proposed to mitigate such leakage, it does not support dynamic data updates. Dynamic SSE (DSSE) addresses this limitation, but directly combining VH-RSSE with DSSE for range queries presents significant challenges, including: (i) lack of backward privacy support, (ii) inaccurate search results, and (iii) high storage and communication overhead. Moreover, current schemes typically require multiple client-server interactions to perform deletions. In this paper, we introduce a volume-hiding range DSSE scheme that is the first to support non-interactive deletion, while concealing the identifier volumes associated with keyword values during query execution. Specifically, we propose the Revocable Inverted Index (RII), which integrates an order-accumulated inverted index and bitmap structures to efficiently handle range queries. By combining RII with DSSE and symmetric revocable encryption, we enable the client to directly manage ciphertext deletions, thus achieving non-interactive deletion and eliminating the need for additional communication overhead. We provide a formal analysis of the leakage functions to demonstrate that our scheme offers the desired security guarantees. Extensive experimental results show that our approach significantly enhances efficiency when compared to existing solutions.

The recently proposed Join Cross-Tags Protocol (JXT) addresses the long-standing issue of excessive query overhead in table joins within Searchable Symmetric Encryption (SSE). As a purely symmetric-key solution, JXT supports efficient conjunctive queries over equi-joins of encrypted tables without requiring any pre-computation during the setup phase. However, JXT has a potential limitation: it may inadvertently reveal the actual volumes of identifiers corresponding to attribute-value pairs, as well as the result values of the join queries. In this paper, we propose JXTMM (JXT multi-map), the first join query scheme designed to hide both volume patterns and result patterns. JXTMM is capable of concealing identifier volumes, preventing the server from learning the actual volumes of attribute-value pairs, and shifting the checkability of join results to the client side, thereby eliminating result pattern leakage. We provide a formal security proof for JXTMM, along with a comprehensive efficiency analysis. Experimental results demonstrate that JXTMM not only performs efficiently on table join queries but also effectively achieves volume-hiding in such queries.

Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.

With the emergence of the sixth-generation (6G) communication technologies, the Internet of Vehicles (IoV) is rapidly developing with the coordination between intelligent networked vehicles, road infrastructures, and the cloud. However, the openness and dynamic nature of the IoV raise significant security and privacy concerns, highlighting the need for efficient authentication schemes. Conventional authentication schemes are no longer suitable for 6G-enabled IoV due to high latency, single point of failure, and heavy management costs. Additionally, existing literature on multi-domain authentication mainly investigates vehicle mobility, ignoring the challenges posed by vehicle heterogeneity. To fill this gap, we propose a multi-domain authentication scheme with conditional privacy preservation (MACPP) that considers administrative domains (AD) and geographic domains (GD) in the IoV. In MACPP, we design a novel identity-based signature scheme without requiring bilinear pairing for efficient authentication. Additionally, we propose a blockchain-assisted pseudonym management scheme (BAPM) to further improve system security by designing a dynamical sparse Merkle tree structure (DSMT). We demonstrate that the proposed MACPP satisfies the security requirements through an in-depth security analysis. Moreover, the experimental results demonstrate the effectiveness and efficiency of both MACPP and BAPM.

Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker (i.e. , sensory data provider) and MCS platform. However, this centralized architecture suffers from poor reliability and difficulties in guaranteeing data quality and privacy, even provides unfair incentives for users. In this paper, we propose a blockchain-based MCS platform, namely BlockSense, to replace the traditional triangular architecture of MCS models by a decentralized paradigm. To achieve the goal of trustworthiness of BlockSense, we present a novel consensus protocol, namely Proof-of-Data (PoD), which leverages miners to conduct useful data quality validation work instead of “useless” hash calculation. Meanwhile, in order to preserve the privacy of the sensory data, we design a homomorphic data perturbation scheme, through which miners can verify data quality without knowing the contents of the data. We have implemented a prototype of BlockSense and conducted case studies on campus, collecting over 7,000 data from workers' mobile phones. Both simulations and real-world experiments show that BlockSense can not only improve system security, preserve data privacy and guarantee incentives fairness, but also achieve at least 5.6x faster than Ethereum smart contracts in verification efficiency.

Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons, and infrastructures and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the centralized architecture adopted in existing IoV systems is fragile to single point-of-failure and malicious attacks. With the emergence of blockchain technology, there is the chance to solve these problems due to its features of being tamper-proof, traceability, and decentralization. In this article, we propose a privacy-preserving vehicular data sharing framework based on blockchain. In particular, we design an anonymous and auditable data sharing scheme using Zero-Knowledge Proof (ZKP) technology so as to protect the identity privacy of vehicles while preserving the vehicular data auditability for Trusted Authorities (TAs). In response to high mobility of vehicles, we design an efficient multi-sharding protocol to decrease blockchain communication costs without compromising the blockchain security. We implement a prototype of our framework and conduct extensive experiments and simulations on it. Evaluation and analysis results indicate that our framework can not only strengthen system security and data privacy but also reduce communication complexity by \(O(\frac{n\sqrt {m}}{m^2})\) times compared to existing sharding protocols.

Satellite internet (Sat-Net) enables high-speed con-nectivity with low latency and extensive coverage. However, it faces challenges related to data security and reliable networking. Blockchain technology, with its decentralized and tamper-proof nature, offers a promising solution to these challenges. However, applying blockchain to the Sat-Net topology is difficult due to its highly dynamic structure, which results in restricted communication periods between satellites and ground stations. To address these issues and adapt to the Sat-Net topology, we propose a solution called RelSharding, which utilizes relayer satellites to relieve concurrent transmission pressure of ground stations by collecting and transmitting data transactions from client satellites. Through caching block headers and Merkle trees in relayer satellites, RelSharding can also reduce authentication latency between satellites utilizing simplified payment verification (SPV). Conducted on the modified SimBlock framework, our experiments indicate that RelSharding can enhance throughput up to 45x and decrease latency by 292x compared to existing blockchain solutions.

Since 2002, hundreds of thousands of vessels have equipped the Automatic Identification System (AIS), which continuously broadcasts its identity and location information for vessel collision avoidance. To utilize these scattered AIS data for further analysis, there are multiple AIS data platforms collecting AIS data from vessels around the world through their satellites and land-based stations. Thus, users can obtain AIS data of vessels from these platforms without dedicated devices. However, existing platforms work in silos, and AIS data is distributed across different platforms, resulting in reduced data availability. In addition, AIS is vulnerable to jamming and spoofing attacks, which can undermine the authenticity of AIS data. In this paper, we propose AISChain, a secure and fast blockchain-based AIS data platform. AISChain adopts consortium blockchain, which only permits those authorized parties (i.e., AIS data providers) to participate in the consensus protocol, and is compatible with current commodity AIS hardware. Since the whole system is co-maintained by multiple authorized parties, AISChain can integrate AIS data resources in a secure way. For avoiding repeated recording of AIS data on the chain, we design the Dynamic Bloom Filter Tree (DBFT) to realize efficient duplication detection in the transaction verification phase. We also propose the dual signature scheme to clarify the AIS data ownership. Moreover, we leverage the geographical location-based blockchain sharding approach to further improve the scalability of AISChain. We implement a prototype of AISChain, and conduct extensive experiments to evaluate the performance of AISChain. Evaluation results show that the search time of DBFT is negligible (4.3 ms) with an extreme low error ratio (0.4%). Meanwhile, AISChain can achieve more than 730 tx/s throughput even when nodes scale to 36. To the best of our knowledge, AISChain is the first work to apply the blockchain technology to secure the AIS data platform.

Recent advances in traffic signal control have witnessed the success of reinforcement learning. However, most of these approaches have focused on vehicle traffic and lack consideration for pedestrians. This can be attributed in part to the fact that the existing underlying technologies are not yet practical to deploy in real-world environments. Vision technologies, for example, can easily be obscured from view in reality. The direction of movement and position of pedestrians is difficult to estimate accurately. The emergence of 6G localization and tracking services offer new opportunities. With this base service, we intend to improve the efficiency, safety, and scalability of multi-intersection traffic signal control with mixed traffic flows. This problem is challenging for its coordination, scalability, and access of new traffic. To solve these challenges, we propose PV-TSC, a distributed reinforcement learning motivated traffic signal control with pedestrian access. We analyze different behaviors of pedestrian traffic, and integrate pedestrian traffic with the proven traffic signal control scheme for vehicle traffic. Finally, we conduct simulation experiments to illustrate the superiority of PV-TSC against classic methods, and further analyze the effectiveness of PV-TSC design by exploring its variants.

Indoor vehicle localization is an underlying technology for realizing Autonomous Valet Parking (AVP), which demands high accuracy and reliability. However, existing localization technologies, such as GPS, WiFi, Bluetooth, suffer from either low availability or high cost, which are not practical in the real world. In order to put AVP into practice, We desperately need an efficient and reliable indoor vehicle localization technology. In this paper, we propose aDeep learning andFM fingerprint map based indoor vehicleLocalization method, namely DeFLoc, which leverages FM signals to achieve accurate and practical indoor localization. In order to reduce the workload of the FM fingerprints collecting process, DeFLoc uses partially uniform sampling to decrease sample data volume and reconstructs the FM fingerprint map from collected incomplete fingerprints precisely using a dedicated deep Convolutional Neural Network (CNN). To alleviate the influence of signal distortions in some FM frequencies, we further design smooth layers in the neural network for improving the accuracy of map reconstruction. Moreover, we devise a continuous vehicle localization algorithm by considering the preferences of vehicle movements to assist us to calibrate localization. We implemented a prototype of DeFLoc and conducted extensive experiments both in simulation and practice. Evaluation results show that our proposed reconstruction model improves accuracy by 40% over conventional matrix completion methods even under the 60% data missing rate. With the precisely reconstructed fingerprint map, DeFLoc achieves over 90% localization accuracy, which indicates DeFLoc can realize accurate and practical indoor vehicle localization.

Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons and infrastructures, and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the centralized architecture adopted in existing IoV systems is fragile to single point of failure and malicious attacks. With the emergence of blockchain technology, it has the chance to solve these problems due to its features of tamper-proof, traceability and decentralization. In this paper, we propose a privacy-preserving vehicular data sharing framework based on blockchain. In particular, we design an anonymous and auditable data sharing scheme using Zero-Knowledge Proof (ZKP) technol-ogy so as to protect the identity privacy of vehicles while preserving the vehicular data auditability for Trusted Authorities (TAs). In response to high mobility of vehicles, we design an efficient multi-sharding protocol to decrease blockchain communication costs without compromising the blockchain security. We implement a prototype of our framework and conduct extensive experiments and simulations on it. Evaluation and analysis results indicate that our framework can not only strengthen system security and data privacy, but also increase the data authenticity verification efficiency by 5x comparing to existing privacy-preserving schemes.

Current studies have shown the effectiveness of long short-term memory network (LSTM) for skeleton-based human action recognition in capturing temporal and spatial features of the skeleton sequence. Nevertheless, it still remains challenging for LSTM to extract the latent structural dependency among nodes. In this paper, we introduce a new long-short graph memory network (LSGM) to improve the capability of LSTM to model the skeleton sequence - a type of graph data. Our proposed LSGM can learn high-level temporal-spatial features end-to-end, enabling LSTM to extract the spatial information that is neglected but intrinsic to the skeleton graph data. To improve the discriminative ability of the temporal and spatial module, we use a calibration module termed as graph temporal-spatial calibration (GTSC) to calibrate the learned temporal-spatial features. By integrating the two modules into the same framework, we obtain a stronger generalization capability in processing dynamic graph data and achieve a significant performance improvement on the NTU and SYSU dataset. Experimental results have validated the effectiveness of our proposed LSGM+GTSC model in extracting temporal and spatial information from dynamic graph data.1

The smart factory is a representative element reshaping conventional computer-aided industry to data-driven smart industry, while it is nontrivial to achieve cost effectiveness, reliability, mobility, and scalability of smart industrial systems. Data-driven industrial systems mainly rely on sensory data collected from statically deployed sensors. However, the spatial coverage of industrial sensor networks is constrained due to the high deployment and maintenance cost. Recently, mobile crowd sensing (MCS) has become a new sensing paradigm owing to its merits, such as cost effectiveness, mobility, and scalability. Nevertheless, traditional MCS systems are vulnerable to malicious attacks and single point of failure due to the centralized architecture. To this end, in this article we integrate MCS with industrial systems without introducing any additional dedicated devices. To overcome the drawbacks of traditional MCS systems, we propose a blockchain-based MCS system (BMCS). In particular, we exploit miners to verify the sensory data and design a dynamic reward ranking incentive mechanism to mitigate the imbalance of multiple sensing tasks. Meanwhile, we also develop a sensory data quality detection scheme to identify and mitigate the data anomaly. We implement a prototype of the BMCS on top of Ethereum and conduct extensive experiments on a realistic factory workroom. Both experimental results and security analysis demonstrate that the BMCS can secure industrial systems and improve the system reliability.

Internet of Things (IoT) plays an indispensable role in our daily life, in many cases, IoT systems are implemented following the client-server paradigm, which are vulnerable to single point of failures and malicious attacks. Due to the resilience and security promise of blockchain, the idea of combining blockchain and IoT has gained considerable attention in recent years. However, blockchains are power-intensive and low-throughput, which may not suitable for power-constrained IoT devices. To tackle these challenges, we present B-IoT, a blockchain based IoT system with credit-based consensus mechanism. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which enhances security and improves transaction efficiency simultaneously. In order to protect the confidentiality of sensitive IoT data, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on a directed acyclic graph (DAG)-structured blockchain, which is more efficient than the satoshi-style blockchain. We implement a prototype of B-IoT on Raspberry Pi, and conduct case studies of a smart factory. Extensive evaluation and analysis results demonstrate that the proposed credit-based PoW mechanism and data access control are practical for IoT.

Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.