DPPT : A differential privacy preservation technique for cyber–physical system

Abstract

Nowadays Cyber–Physical System (CPS) is implemented in several areas, such as health care, agriculture, transportation, etc. The main aim of CPS is for automation or minimization of human interaction. The conventional Machine Learning (ML) based approach involves sensitive issues such as information leakage. That issue is resolved using the Federated Learning (FL) based approach partially. The FL uses a decentralized environment for training the models, where models are trained locally, and only the updated part is sent to the data center or cloud server. It improves data privacy as the clients send only the local updates (model gradients). But also there is a high risk of data leakage while publishing the data in FL based System. A Differential Privacy Preservation Technique (DPPT) is an efficient technique to preserve information leakage. In this paper, a differential privacy preservation technique has been proposed by adding random noise to a data sample in order to generate anonymity. The performance of the model, and data quality are evaluated as privacy preservation techniques often degrade data quality.