Intelligent Data Analysis 21 (2017) S21–S39 S21 DOI 10.3233/IDA-170870 IOS Press Analysis of privacy and utility tradeoffs in anonymized mobile context streams Shyue-Liang Wang a,∗ , Min-Jye Hsiu a , Yu-Chuan Tsai b , I-Hsien Ting a and Tzung-Pei Hong c 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 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 effec- tiveness 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 1. Introduction Mobile phones are ubiquitous. The number of mobile phone users worldwide is about 4.43 billion in 2015, which is about 59.9% of global population, and is expected to increase to 5.07 billion in 2019 at around 68.5% of global population. Nearly two-fifths of all mobile phone users (1.75 billion) – close to one quarter of the world population-use a smartphone at least monthly in 2014 [23]. The number of available apps in the Apple App Store rises from 300,000 in October 2010 to 1,500,000 in 2015 [24], with comparable amount in Google Play of 2,000,000 in February 2016, which together account for over 90% of worldwide app revenue. The ease of use and free of use of some apps not only make sharing information so popular, simple and easy, but also potentially expose personal identifiable information to public, such as full name, email address, phone numbers, sex, birthday, as well as hobbies, interests and information, when they are tied to individually identifiable information. In addition, context-aware apps which provide personalized services typically collect data through onboard or externally connected body-worn sensors that are capable of tracking locations, social ∗ Corresponding author: Shyue-Liang Wang, Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan. E-mail: slwang@nuk.edu.tw. 1088-467X/17/$35.00 c  2017 – IOS Press and the authors. All rights reserved