Decaying Trust: Implementing Privacy through Data Lifespan Management in Medical Cyber Physical Systems

Abstract

An Cyber-Physical Systems (CPS) in healthcare offer immense potential for personalized medicine and remote monitoring, but raise critical concerns regarding temporal privacy. This paper explores the challenges of long-term data retention in medical CPS and proposes a privacy-preserving framework using data decay mechanisms. We address the dynamic nature of data sensitivity by employing an exponential decay algorithm, which gradually reduces data granularity over time, balancing utility and privacy. We further discuss the importance of classifying data based on sensitivity levels and propose a high-level architectural design for implementing temporal privacy preservation in medical CPS. Future directions include adaptive decay rates, personalized decay policies, integration with federated learning, and blockchain-based auditing. These advancements aim to create a robust and trustworthy framework that protects patient privacy while enabling the full potential of medical CPS. By prioritizing temporal privacy, the responsible and ethical deployment of these transformative methods can be ensured effectively.