Analysis of privacy and utility tradeoffs in anonymized mobile context streams

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.