In the Machine-as-a-Service (MaaS) model, enterprises can significantly reduce production costs by leasing devices from original equipment manufacturers (OEM), while OEM can enhance device quality by utilizing device data shared by enterprises. As such, MaaS is emerging as a very promising paradigm in modern manufacturing. However, the multidimensional data trend formed by the multidimensional data may leak the private production data of enterprises, particularly when the OEM leases the same type of device to enterprises. Currently, there is no targeted and feasible solution to ensure the privacy, integrity, and fault-tolerant of multi-user and multidimensional data in the MaaS model. To address this challenge, we propose a lightweight, privacy-preserving and fault-tolerant distributed multidimensional data trend query scheme for MaaS. The proposed scheme ensures multidimensional data privacy through local differential privacy (LDP), and guarantees fault-tolerant and data integrity using Shamir secret sharing and hash-based message authentication code (mac). To protect the privacy of multidimensional data aggregation trend, we design a weighted noise injection query algorithm based on LDP. Additionally, the scheme mitigates the risk of data leakage by introducing the blockchain (BC) instead of cloud server (CS). We formally prove the security of our proposed scheme, and the experimental evaluation demonstrates that it outperforms existing schemes in terms of computation and communication overhead.

Authentication is an important security issue for multi-access edge computing (MEC). However, the existing authentication schemes have not achieved a good balance between privacy preserving, efficiency, and low computation overhead on the device side. To address this issue, we propose an efficiency-improved and conditional privacy-preserving authentication scheme suitable for resource-constrained MEC devices. Our core idea is integrating the merkle hash tree (MHT) into the anonymous authentication scheme constructed by the blockchain and key derivation function (KDF) to improve efficiency. The MHT not only reduces the on-chain storage overhead brought by the increasing pseudo-public keys of KDF, but also utilizes few hash functions to achieve lightweight${\bm {k}}$-times authentications with the same edge server. Despite these advantages, managing pseudo-key pairs in the form of MHT leafs still brings efficiency and unlinkability problems. We construct the partially shuffled merkle hash tree to only shuffle leafs within the device group, and combine with the KDF to update MHTs in a public manner by synchronizing pseudo-key pairs. Consequently, the efficiency of key update can be ensured. Moreover, a time-bound key derivation function based on physically unclonable function and BIP-32 is developed to provide immediate and permanent device revocation. Only the remaining valid pseudo-public keys of the revoked device will be recorded on the blockchain, which reveals no linkable information and avoids frequently reconstructing all the MHTs. We prove the authentication security and discuss other security features. A proof-of-concept prototype was implemented to conduct experiments and comparative analysis for performance evaluation.

Authentication is an important security issue for multi-access edge computing (MEC). To restrict user access from untrusted devices, Bring Your Own Device (BYOD) policy has been proposed to authenticate users and devices simultaneously. However, when integrating BYOD policy into MEC authentication to improve security, issues of efficient binding and user-device conditional anonymity have not been well supported. To address these issues, we propose Bring Your Device Group (BYDG) policy by constructing efficient and privacy-preserving user-device authentication. Our core idea is to use key sequences generated by PUFs-based key derivation functions (KDFs) to not only construct efficient binding relationships, but also achieve conditional anonymity for device groups. Specifically, a flexible and secure binding method is first developed by leveraging Chinese Remainder Theorem (CRT) to bind user with device groups. Each device’s CRT modulus is derived from the key sequence to construct many-to-many user-device binding relationships, which are managed in the form of on-chain Pedersen Commitment. Moreover, we design an identity anonymizing and tracing method for device groups. The key sequence is regarded as traceable device pseudo-identities, and then inserted into the cuckoo filter to reduce the on-chain storage overhead and mitigate malicious login attempts with low costs. Based on above two methods, the combination of Pedersen Commitment and Zero-Knowledge Proof of Knowledge is used to achieve user-device authentication with conditional anonymity. The security analysis was presented to demonstrate important security properties. A proof-of-concept prototype was implemented to conduct performance evaluation and comparative analysis.

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

The blockchain‐based management system has been regarded as a novel way to improve the efficiency and safety of Vehicular Ad Hoc Networks (VANETs). A blockchain‐based scheme’s performance depends on blockchain nodes’ computing power composed from the road‐side unit (RSU). However, the throughput of blockchain‐based application in VANETs is limited by the network bandwidth. A single blockchain cannot record large‐scale VANETs’ data. In this paper, we design an atomic cross‐chain swap‐based management system (ACSMS) to boost the scalability of blockchain‐based application in VANETs. The blockchain‐based public‐key encryption with keyword search is further introduced to protect user privacy. The analysis shows that ACSMS achieves cross‐chain swap without loss of CAV security privacy. The simulation results show that our method can realize multiple blockchain‐based applications in VANETs.