CAP2M..÷Contingent Anonymity Preserving Privacy Method for the Internet of Things Services

Abstract

The Internet of Things (IoT) consists of a collection of inter-connected devices that are used to transmit data. Secure transactions that guarantee user anonymity and privacy are necessary for the data transmission process. One of the major constraints of IoT Healthcare is the issue of maintaining security. These problems are addressed in this study by the Contingent Anonymity-Preserving Privacy Method (CAP2M), which takes into account the various security needs. The CAP2M method uses the session deployment and user requirements to preserve user privacy. The initial and final privacy settings are modified in the session deployment depending on the session interval and the service provider's recommendation. Federated learning is employed for training privacy settings toward the session's final interval. The suggested approach maintains privacy on various sharing intervals with minimum computation complexity. In the privacy-preserving process, two-factor authentication is used for concealing the sharing and shared ends. The performance is validated using the metrics failure, complexity, and latency.