IMPACT OF NOISE CUSTOMIZATION IN DIFFERENTIAL PRIVACY FOR CYBER PHYSICAL SYSTEMS

Abstract

The integration of Cyber Physical Systems (CPS) poses significant privacy challenges due to the amalgamation of computational and physical processes. Differential Privacy (DP) emerges as a crucial framework to safeguard individual privacy while extracting meaningful insights from CPS data. This study delves into the nuanced impact of noise customization within the DP paradigm in the realm of CPS. Noise customization involves the strategic addition of calibrated noise to data, influencing the delicate balance between privacy preservation and data utility. We explore the diverse types of noise, such as Laplace, Gaussian, and adaptive scaling, evaluating their impact on privacy, data accuracy, and system robustness. The suitability of noise customization is analyzed in varying data distributions, addressing the dynamic nature of CPS environments. Benefits encompass tailored privacy protection, adaptability to changing conditions, and optimized communication overhead. However, challenges arise in striking the right balance between privacy and utility, particularly in fine-grained analyses. This research underscores the importance of customized noise in fortifying the privacy fabric of CPS, providing insights into the trade-offs inherent in privacy-preserving data analytics within this complex and dynamic cyberphysical landscape