Privacy of Deep Learning Systems: A Penetration Testing Framework

The advent of deep learning has revolutionized various data-driven fields such as image recognition, natural language processing, and autonomous vehicles. Despite its transformative potential, deep learning raises significant privacy concerns, particularly regarding the handling of sensitive data during training and inference. This study systematically reviews existing literature on privacy-preserving techniques in deep learning systems, addressing three primary research questions: the main privacy concerns, the effectiveness of current penetration testing techniques, and the mitigation strategies to enhance privacy. Privacy concerns primarily revolve around the risk of exposing sensitive training data and internal model parameters through attacks like model inversion. Differential privacy and homomorphic encryption are widely employed to mitigate these risks, although challenges remain in balancing privacy with model utility. Penetration testing techniques, such as adversarial attack simulations and differential privacy analysis, play a crucial role in identifying vulnerabilities but often lack comprehensive coverage across all stages of a deep learning system's lifecycle. Mitigation strategies following penetration testing include robust data anonymization, encryption, differential privacy mechanisms, and federated learning to protect data during transfer and storage. Continuous monitoring, regular audits, and incident response procedures are also essential to maintain privacy standards and ensure system resilience. This research highlights the need for integrating comprehensive privacy measures throughout the lifecycle of deep learning systems. Future research directions include the development of more effective penetration testing methodologies and enhanced privacy-preserving algorithms to safeguard sensitive data and maintain user trust.