Scalable Unified Privacy-Preserving Machine Learning Framework (SUPM)

Abstract

The widespread use of IoT devices is expected to enable the collection and utilization of a variety of data, including personal health information. For example, we could provide our personal information for machine learning operated by an external server, which in return detects signs of illness. However, it is necessary to protect privacy of personal information. Precisely, there are two issues in privacy preserving machine learning. One is data privacy which means to protect our privacy to external servers. The other is model privacy which means to protect our privacy from models. Local differential privacy (LDP) mechanisms have been proposed as a method to provide personal sensitive information to external servers with privacy protection. LDP mechanisms can ensure privacy by adding noise to data, but on the other hand, adding noise reduces their usefulness for analysis. In this paper, we propose a privacy-preserving machine learning framework, which can deal with both data privacy and model privacy. We also propose a LDP-mechanism framework which can deal with various attributes included in a single data. We also make sure feasibility of our mechanism in two cases of breast cancer screening data and ionosphere data set.