LAUSR

Towards the massive amount of data generated in our daily work and life, embedded systems, with economical but powerful storage and computing resources, are inevitably becoming the most suitable platform for the Edge Computing for the Internet of Things. However, embedded system servers may also threaten individuals by storing individuals’ private data for years. This paper proposes a Resilient Tag-based Privacy Protection (RTPP) scheme for embedded systems. Specifically, to protect the privacy against the hackers and other non-users, we employ a pseudo-random number encryption technique with the chaos-based principle so that the third party cannot easily steal the private data and reduce the risk of personal privacy leakage. To protect the individuals’ interests, we propose a new approach to controlling the life cycle table of data to enable individuals themselves the flexibility to control the life cycle of private data. Unlike existing data lifetime management methods, the RTPP can support the retrieval of tags in the data life cycle table to control the corresponding privacy while automatically adding or removing tags. Our system automatically adjusted the survival period of private data in the life cycle table through the change of leaf weights, controlled the charge movement on the surface of flash memory, and finally achieved the resilient adjustment process of the life cycle of private data in the embedded system. The security proof and performance evaluation show that the proposed RTPP scheme is provable secure in the automatic privacy lifecycle tuning model for embedded systems and efficient in practice.