02000nas a2200253   4500008004100000022001300041245010400054210006900158260001200227300001100239490000700250520118500257653002701442653001801469653001601487653002501503653002001528100003001548700001501578700001901593700001901612700002901631856008601660       2018                        eng d  a1550485900aA Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems0 aFramework for PrivacyPreserving Data Publishing with Enhanced Ut  c12/2018  a1 - 220 v143 a<p>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.</p>10acyber physical systems10adeep learning10ak-anonymity10aPrivacy preservation10aSmart buildings1 aSangogboye, Fisayo, Caleb1 aJia, Ruoxi1 aHong, Tianzhen1 aSpanos, Costas1 aKjærgaard, Mikkel, Baun  uhttps://simulationresearch.lbl.gov/publications/framework-privacy-preserving-data