LPP-CNN: A Lightweight Privacy-Preserving Object Classification Framework for Military Vehicle Images

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.