Affiliations: [a] Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan | [b] Library and Information Center, National University of Kaohsiung, Kaohsiung, Taiwan | [c] Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan
Correspondence:
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Corresponding author: Shyue-Liang Wang, Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan. E-mail:[email protected]
Abstract: Mobile user data are collected by service providers around the clock and
through intelligent data analysis in which it can offer great services for
health cares, business activities, and other personal or social services,
etc. However, data could be misused and privacy could potentially be
breached which might lead to harmful consequences. Many privacy-preserving
techniques have been proposed in the past decade for anonymizing relational
and social data. But only a handful of privacy-preserving techniques have
been proposed to anonymize sensitive mobile context before releasing data to
service providers. Unfortunately, these techniques also reduce the utility
of data that are supposed to provide helpful services. As such, the
effectiveness of these anonymization techniques cannot be easily justified
and compared. In this work, we propose a unified approach to define privacy
gain and utility loss due to anonymizing sensitive context on mobile user
data. We further perform extensive numerical evaluation on various
well-known anonymization techniques, compare their performances and
trade-offs between privacy and utility, and also provide a framework of
analysis which serves a reference for adopting suitable anonymization
technique for different user requirements.
Keywords: Mobile privacy, anonymization, privacy utility tradeoff, mobile context, Markov chain
