Journal
ACM TRANSACTIONS ON SENSOR NETWORKS
Volume 14, Issue 3-4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3275520
Keywords
Privacy preservation; k-anonymity; smart buildings; deep learning; cyber-physical systems
Funding
- Republic of Singapore's National Research Foundation
- Innovation Fund Denmark [4106-00003B]
- Office of Energy Efficiency and Renewable Energy, the U.S. Department of Energy [DE-AC02-05CH11231]
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Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.
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