In this paper, we propose a lightweight privacy-preserving convolutional neural network framework for military vehicle images classification (LPP-CNN). Existing target classification methods primarily focus on improving accuracy and recognition efficiency but often overlook security threats during data transmission and processing, making them unsuitable for high-risk battlefield scenarios. Although some approaches incorporate image security through homomorphic encryption, the low efficiency and operational complexity of such techniques hinder their applicability in battlefield environments. To this end, we design a solution that integrates additive secret sharing with edge computing by encrypting images into two ciphertexts, which are then processed independently by two edge servers to prevent data leakage during upload. The encrypted data is subsequently processed by the LPP-CNN embedded with secure computation protocols. Finally, results are combined and decrypted to achieve target classification. This method ensures efficient and accurate military vehicle classification while significantly enhancing data privacy and security. Theoretical analysis demonstrates improved response speed and reduced communication overhead. Experimental evaluations show that under a classification accuracy not less than 94%, the computational efficiency of SComp, SReLU, and SMaxPool protocols increase by 32, 4, and 0.5 times, respectively, while communication costs decrease by 2, 2, and 13 times. The overall framework achieves a 33% improvement in computational efficiency compared to existing solutions. Compared to state-of-the-art methods, our framework not only meets battlefield requirements for high-precision object classification but also substantially strengthens data privacy and security, aligning more effectively with real-world military application demands.

Cloud storage can provide a way to effectively store and manage big data. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in a traditional way, which leads to the introduction of the auditing techniques. This paper proposes a public auditing protocol with a self-certified public key system using blockchain technology. The user's operational information and metadata information of the file are formed to a block after verified by the checked nodes and then to be put into the blockchain. The chain structure of the block ensures the security of auditing data source. The security analysis shows that attackers can neither derive user’s secret key nor derive users’ data from the collected auditing information in the presented scheme. Furthermore, it can effectively resist against not only the signature forging attacks but also the proof forging attacks. Compared with other public auditing schemes, our scheme based on the self-certified public key system has been improved in storage overhead, communication bandwidth, and verification efficiency.

Location-based services (LBS) applications provide convenience for people’s life and work, but the collection of location information may expose users’ privacy. Since these collected data contain much private information about users, a privacy protection scheme for location information is an impending need. In this paper, a protection scheme DPL-Hc is proposed. Firstly, the users’ location on the map is mapped into one-dimensional space by using Hilbert curve mapping technology. Then, the Laplace noise is added to the location information of one-dimensional space for perturbation, which considers more than 70% of the nonlocation information of users; meanwhile, the disturbance effect is achieved by adding noise. Finally, the disturbed location is submitted to the service provider as the users’ real location to protect the users’ location privacy. Theoretical analysis and simulation results show that the proposed scheme can protect the users’ location privacy without the trusted third party effectively. It has advantages in data availability, the degree of privacy protection, and the generation time of anonymous data sets, basically achieving the balance between privacy protection and service quality.

Location‐based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n‐order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.