Where You Go Matters

被引:0
|
作者
Baron B. [1 ]
Musolesi M. [1 ,2 ,3 ]
机构
[1] University College London, Bloomsbury, London
[2] Alan Turing Institute, London
[3] University of Bologna, Bologna
基金
英国工程与自然科学研究理事会;
关键词
Location tracking; personal information inference; self check-in mobile systems;
D O I
10.1145/3432699
中图分类号
学科分类号
摘要
Data gathered from smartphones enables service providers to infer a wide range of personal information about their users, such as their traits, their personality, and their demographics. This personal information can be made available to third parties, such as advertisers, sometimes unbeknownst to the users. Leveraging location information, advertisers can serve ads micro-Targeted to users based on the places they visited. Understanding the types of information that can be extracted from location data and implications in terms of user privacy is of critical importance. In this context, we conducted an extensive in-The-wild research study to shed light on the range of personal information that can be inferred from the places visited by users, as well as privacy sensitivity of the personal information. To this end, we developed TrackingAdvisor, a mobile application that continuously collects user location and extracts personal information from it the app also provides an interface to give feedback about the relevance of the personal information inferred from location data and its corresponding privacy sensitivity. Our findings show that, while some personal information such as social activities is not considered private, other information such as health, religious belief, ethnicity, political opinions, and socio-economic status is considered private by the participants of the study. This study paves the way to the design of privacy-preserving systems that provide contextual recommendations and explanations to help users further protect their privacy by making them aware of the consequences of sharing their personal data. © 2020 ACM.
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