Article
Privacy-Preserving Data Leakage Detection Using Homomorphic Encryption and Deep Learning
Protecting sensitive information from unauthorized exposure has become a critical challenge in modern cybersecurity environments. Conventional data leakage detection mechanisms often require access to unencrypted content, creating potential privacy and security risks during the inspection process. This paper proposes a privacy-aware data leakage detection framework that integrates Fully Homomorphic Encryption (FHE) with advanced deep learning techniques to enable secure analysis of protected data without revealing its original content. The proposed model, named HE-DLDNet, utilizes a transformer-inspired neural architecture specifically adapted to process encrypted information while maintaining strong confidentiality guarantees. By performing detection directly on encrypted data streams, the framework eliminates the need for decryption during analysis and significantly reduces the risk of sensitive information exposure. Extensive experimental evaluation demonstrates that the proposed approach achieves a detection accuracy of 94.3% while maintaining practical computational performance. Performance comparisons indicate that the framework lowers processing overhead by approximately 73% relative to existing homomorphic encryption-based detection methods. In addition, the system supports near real-time monitoring with response delays below 250 milliseconds, making it suitable for deployment in enterprisescale environments. The results highlight the effectiveness of combining privacy-preserving cryptographic techniques with intelligent learning models to build secure and efficient data leakage detection solutions. This research contributes toward the development of next-generation cybersecurity systems that balance strong privacy protection with accurate threat identification.
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