Achieve data privacy and clustering accuracy simultaneously through quantized data recovery

EURASIP journal on advances in signal processing

article

Wang, Ren¹  Wang, Meng¹  Xiong, Jinjun² 

[1] Department of Electrical, and Systems Engineering, Rensselaer Polytechnic Institute;IBM Thomas J. Watson Research Center

关键词: Subspace clustering;    Quantization;    Data recovery;    Data privacy;    Smart meter;   

DOI  :  10.1186/s13634-020-00682-7

来源: SpringerOpen

【 摘 要 】

This paper develops a data collection and processing framework that achieves individual users’ data privacy and the operator’s information accuracy simultaneously. Data privacy is enhanced by adding noise and applying quantization to the data before transmission, and the privacy of an individual user is measured by information-theoretic analysis. This paper develops a data recovery and clustering method for the operator to extract features from the privacy-preserving, partially corrupted, and partially observed measurements of a large number of users. To prevent cyber intruders from accessing the data of many users, it also develops a decentralized algorithm such that multiple data owners can collaboratively recover and cluster the data without sharing the raw measurements directly. The recovery accuracy is characterized analytically and showed to be close to the fundamental limit of any recovery method. The proposed algorithm is proved to converge to a critical point from any initial point. The method is evaluated on recorded Irish smart meter data and UMass smart microgrid data.

【 授权许可】

CC BY