A New Weight and Sensitivity Based Variable Maximum Distance to Average Vector Algorithm for Wearable Sensor Data Privacy Protection

• Main Authors: Zhenjiang Zhang, Bowen Han, Han-Chieh Chao, Feng Sun, Lorna Uden, Di Tang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Wearable device; privacy protection; micro aggregation; differential privacy
• Online Access: https://ieeexplore.ieee.org/document/8756281/
• ISSN: 2169-3536

The problem of privacy protection of wearable devices when publishing data can be solved based on the variable-maximum distance average vector. This paper proposes a new weight and sensitivity based variable maximum distance average vector (WSV-MDAV) method aiming to solve the problems that may be contained in the existing privacy protection algorithm. The proposed approach considers the difference of the importance among all the identifiers by setting corresponding weight coefficient W. Given a specific weight to each attribute in the table, we can subsequently get a distance metric based on weight. Similarly, the different sensitivity constraint S for different sensitive attributes is also available for our proposed method. Using the WSV-MDAV algorithm we propose a new privacy protection model for the data publishing of wearable device by introducing the concept of the differential privacy. The numerical results show that the proposed WSV-MDAV algorithm improves the privacy protection performance and reduces the information loss compared to the traditional method.