research-article

Where You Go Matters: A Study on the Privacy Implications of Continuous Location Tracking

Authors:

Benjamin Baron,

Mirco MusolesiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 4

Article No.: 169, Pages 1 - 32

https://doi.org/10.1145/3432699

Published: 18 December 2020 Publication History

Abstract

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.

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Cited By

Cunha MDuarte GAndrade RMendes RVilela JVilela JSchulmann HLi N(2024)Privkit: A Toolkit of Privacy-Preserving Mechanisms for Heterogeneous Data TypesProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653284(319-324)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653284
Watson JDespres TTan APatil SDutta PPopa R(2024)Nebula: A Privacy-First Platform for Data Backhaul2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00092(3184-3202)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00092
Khalili HChien HHass ASehatbakhsh N(2024)Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT DevicesIEEE Internet of Things Journal10.1109/JIOT.2023.328852311:1(1054-1064)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3288523
Show More Cited By

Index Terms

Where You Go Matters: A Study on the Privacy Implications of Continuous Location Tracking
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing
  2. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Smartphones
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 4

December 2020

1356 pages

EISSN:2474-9567

DOI:10.1145/3444864

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Copyright © 2020 ACM.

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Publication History

Published: 18 December 2020

Published in IMWUT Volume 4, Issue 4

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Cited By

Cunha MDuarte GAndrade RMendes RVilela JVilela JSchulmann HLi N(2024)Privkit: A Toolkit of Privacy-Preserving Mechanisms for Heterogeneous Data TypesProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy10.1145/3626232.3653284(319-324)Online publication date: 19-Jun-2024
https://dl.acm.org/doi/10.1145/3626232.3653284
Watson JDespres TTan APatil SDutta PPopa R(2024)Nebula: A Privacy-First Platform for Data Backhaul2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00092(3184-3202)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00092
Khalili HChien HHass ASehatbakhsh N(2024)Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT DevicesIEEE Internet of Things Journal10.1109/JIOT.2023.328852311:1(1054-1064)Online publication date: 1-Jan-2024
https://doi.org/10.1109/JIOT.2023.3288523
Corrias RGjoreski MLangheinrich M(2023)Exploring Transformer and Graph Convolutional Networks for Human Mobility ModelingSensors10.3390/s2310480323:10(4803)Online publication date: 16-May-2023
https://doi.org/10.3390/s23104803
Ayalon OLi SPreneel BRedmiles E(2023)Not Only for Contact TracingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35703486:4(1-26)Online publication date: 11-Jan-2023
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Gjoreski MLaporte MLangheinrich M(2022)Toward privacy-aware federated analytics of cohorts for smart mobilityFrontiers in Computer Science10.3389/fcomp.2022.8912064Online publication date: 27-Jul-2022
https://doi.org/10.3389/fcomp.2022.891206
Oliver MRossi ACohn J(2022)Know Thyself as a Virtual RealityThe International Review of Information Ethics10.29173/irie48131:1Online publication date: 22-Aug-2022
https://doi.org/10.29173/irie481
Vinayaga-Sureshkanth NWijewickrama RMaiti AJadliwala MJadliwala MKim YDmitrienko A(2022)An Investigative Study on the Privacy Implications of Mobile E-scooter Rental AppsProceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks10.1145/3507657.3528551(125-139)Online publication date: 16-May-2022
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