Privacy-Preserving Data Analysis without Trusted Third Party
Atsuko Miyaji, Tomoka Takahashi, Ping-Lun Wang, Tatsuhiro Yamatsuki, Tomoaki Mimoto · 2022
With the spread of IoT devices, various data about our lives are being collected, such as heart rate, physical activity, number of steps, pulse, oxygen intake, calorie consumption, etc. If these data can be analyzed, it will be possible to learn the signs of disease. However, it is dangerous for a person’s activity status to be managed on an external server with the view of privacy. To solve this problem, local differential privacy (LDP), which is a technique for randomly adding local noise to data, has been proposed. While ensuring privacy by LDP is certainly important, it degrades the usefulness of the analysis of data with added noise. In this paper, we propose a new mechanism to protect data privacy in both phases of training and testing based on LDP. We also make sure feasibility of our mechanism in two cases of breast cancer screening data and ionosphere data set.