Vehicle platoon (VP), as a typical form of traffic cooperation, can significantly enhance traffic efficiency and safety in Vehicular Ad hoc Networks (VANETs). However, malicious vehicles in VP poses a severe threat to the security of entire VP, requiring to be efficiently traced by identity authentication. In this paper, we propose a lattice-based efficient and traceable privacy-preserving batch authentication scheme for vehicle platoon in VANETs, named LETA. First, we design a dynamic VP identity structure VPD-Tree which is constructed based on hash tree and pseudonyms of vehicles to preserve privacy. Then, an aggregate signature is constructed based on VPD-tree and modular lattice for secure and efficient batch authentication of VP. Finally, Zero-Knowledge Proofs (ZKP) is applied on the VPD-Tree structure to anonymously and efficiently trace the malicious vehicles of VP. Security analysis shows that LETA achieves stronger security guarantees, thereby offering a more secure solution than existing approaches. Moreover, performance evaluations show that LETA achieves lower computation and communication overheads through the VPD-tree structure and efficient batch authentication scheme.

Incentive (or point) systems are widely deployed across industries such as retail, tourism, and finance to enhance customer loyalty and create benefits for service providers. However, their operation typically requires the collection and processing of sensitive customer data, leading to significant privacy concerns. Existing privacy-preserving incentive systems predominantly rely on bilinear pairings and the discrete logarithm assumption, which, while efficient in classical settings, are vulnerable to quantum adversaries and thus lack long-term security guarantees. To address this limitation, we present LatInc, a practical lattice-based privacy-preserving incentive system. LatInc integrates state-of-the-art lattice-based signatures with efficient protocols, the ABDLOP commitment, and efficient lattice zero-knowledge proofs, achieving a robust balance between post-quantum security and efficiency. Relying on the hardness of the MLWE and MSIS problems, we formally prove that LatInc achieves unforgeability, anonymity, and framing-resistance in the random oracle model. We implement a demo of the system and evaluate its performance on a standard laptop platform. Experimental results show that the communication overheads for the Earning and Spending protocols are approximately 99 KB and 140 KB, respectively, with execution times of 610 ms and 900 ms, highlighting significant efficiency gains over previous lattice-based incentive constructions.

In Vehicular Ad-hoc Networks (VANETs), federated learning (FL) enables collaborative training a global intelligent transportation model without sharing vehicles’ raw data. Achieving model convergence in VANETs requires multiple rounds of FL for aggregation and updates. To improve the efficiency of model convergence, researchers explore methods of multi-round aggregation. The latest work, Flamingo (S&P 2023), proposes a multi-round aggregation scheme based on a reusable key mechanism. Its application in VANETs with dynamic network structures enhances the efficiency of model training. However, this scheme has a defect: vehicles cannot verify the correctness of the aggregation result. Once a semi-trusted server returns incorrect aggregation results due to computational errors, it may reduce the model’s accuracy or even cause training failure. In this scheme, we propose a verifiable multi-round aggregation scheme for FL in VANETs (VMFL), enabling vehicles to verify the aggregation results. Firstly, a multi-round verification mechanism is designed to reduce the number of interactions for vehicles by using reusable keys. Additionally, we propose a lightweight proof scheme that allows vehicles to verify the results with minimal computation, reducing the computational overhead of the verification process. Finally, a security analysis of VMFL is performed to demonstrate its security in cases of vehicle dropout. We validated the efficiency of VMFL across different datasets through experiments, showing no significant increase in time overhead compared to Flamingo, and demonstrating that its verification time is reduced by about 90% compared to the state-of-the-art scheme, thereby demonstrating the scheme’s usability.

Vehicle platoon has attracted much attention in recent years for its benefits in improving traffic efficiency, road safety, and energy consumption. In a vehicle platoon, vehicles can take on the roles of either a leading vehicle or a following vehicle, depending on the need. Selecting a reliable leading vehicle is crucial to improve the reliability of the vehicle platoon, and reputation management plays a vital role in this selection process. However, the existing reputation management schemes do not distinguish the leading vehicle's reputation value (LV-Reputation value) and the following vehicle's reputation value (FV-Reputation value), and merge them into a single reputation value, which exposes the reputation management scheme to the single reputation attack, an attack identified for the first time in this work. Additionally, some schemes fail to preserve the vehicle reputation value privacy, feedback score privacy, or identity privacy, and some overlook the security of the reputation management scheme. To address these issues, we propose a Privacy-Preserving Dual Reputation (PPDR) management scheme in vehicle platoon. The PPDR scheme separates a vehicle's reputation value into LV-Reputation value and FV-Reputation value, effectively mitigating the single reputation attack. Furthermore, under the premise of preserving reputation value privacy, feedback score privacy, and identity privacy, the PPDR scheme employs a score difference calculation algorithm to support the weighted average mechanism and the dual reputation management mechanism, which can significantly improve the accuracy of reputation management scheme. It also provides strong security with acceptable computation communication overheads. Comprehensive theoretical analysis and simulation evaluation have been carried out, and the results demonstrate that the PPDR scheme is significantly superior than the existing schemes in several aspects.

This study aims to address the problem of medical text data privacy protection in the context of extensive development of cloud computing. This paper comprehensively researches the relevant literature and technical progress in recent years on privacy protection in medical scenarios using homomorphic encryption and other advanced cryptography techniques, and synthesizes the scalability of cloud computing, the timeliness of edge computing, and the inerrancy of blockchain technology to achieve the privacy protection of medical text data through the cloud storage of medical data and the dense state computation, and protects the privacy of patient's medical data in order to achieve the purpose of protecting individual privacy, property security, and security. It protects the privacy of patients' medical data to realize the purpose of protecting personal privacy and property security. By changing the encryption parameters of the existing DGHV multi-key homomorphic encryption scheme to realize the characteristics of the adapted scheme, and at the same time, the public key parity restriction can be eliminated. In addition, this paper also discusses the application of blockchain technology in homomorphic encryption key management, and proposes a blockchain-based key security storage system to ensure the key's tamperability and security. The privacy security problem in medical data transmission, storage and processing is solved by constructing this medical text data privacy protection system based on cloud-edge cooperative computing and homomorphic encryption. Simulation experiments verify that this scheme is practically feasible, while the correct parameters and the choice of the edge computing approach take into account both security and feasibility.

With the increasing severity of user privacy leaks in online social networks (OSNs), existing privacy protection technologies have difficulty meeting the diverse privacy protection needs of users. Therefore, privacy‐aware (PA) for the text data that users post on OSNs has become a current research focus. However, most existing PA algorithms for OSN users only provide the types of privacy disclosures rather than the specific locations of disclosures. Furthermore, although named entity recognition (NER) technology can extract specific locations of privacy text, it has poor recognition performance for nested and interest privacy. To address these issues, this paper proposes a PA framework based on the extraction of OSN privacy information content. The framework can automatically perceive the privacy information shared by users in OSNs and accurately locate which parts of the text are leaking sensitive information. Firstly, we combine the roformerBERT model, BI_LSTM model, and global_pointer algorithm to construct a direct privacy entity recognition (DPER) model for solving the specific privacy location recognition and entity nesting problems. Secondly, we use the roformerBERT model and UniLM framework to construct an interest privacy inference (IPI) model for interest recognition and to generate interpretable text that supports this interest. Finally, we constructed a dataset of 13,000 privacy‐containing texts for experimentation. Experimental results show that the overall accuracy of the DPER model can reach 91.80%, while that of the IPI model can reach 98.3%. Simultaneously, we compare the proposed model with recent methods. The analysis of the results indicates that the proposed model exhibits better performance than previous methods.