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FindYou: A Personal Location Privacy Auditing Tool

Authors:

Christopher Riederer,

Daniel Echickson,

Stephanie Huang,

Augustin ChaintreauAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

Pages 243 - 246

https://doi.org/10.1145/2872518.2890546

Published: 11 April 2016 Publication History

Abstract

The ubiquitous availability of location data to smartphone apps and online social networks has caused the collection of such information to grow at an unprecedented rate. However, the discriminative power and potential uses of this data collection is not always clear to the end user. In this work, we present FindYou, a web-based application that gives users the ability to perform a location data privacy audit. FindYou lets users import and visualize the location data collected by popular web services in order to understand what these companies know or can easily infer about them. Additionally, FindYou gives users the option to donate their data to the scientific community, creating new mobile datasets linked to user properties that will be open to use by academic institutions. We hope that FindYou will increase awareness of the privacy issues surrounding the collection and use of location data, the potential problem of "digital red-lining", and also create valuable new datasets with the full informed consent of interested users.

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Cited By

Erickson JYuzon JBonaci T(2022)What You Do Not Expect When You Are Expecting: Privacy Analysis of FemtechIEEE Transactions on Technology and Society10.1109/TTS.2022.31609283:2(121-131)Online publication date: Jun-2022
https://doi.org/10.1109/TTS.2022.3160928
Schufrin MReynolds SKuijper AKohlhammer J(2021)A Visualization Interface to Improve the Transparency of Collected Personal Data on the InternetIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302894627:2(1840-1849)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3028946
Schufrin MReynolds SKuijper AKohlhammer J(2020)A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet2020 IEEE Symposium on Visualization for Cyber Security (VizSec)10.1109/VizSec51108.2020.00007(1-10)Online publication date: Oct-2020
https://doi.org/10.1109/VizSec51108.2020.00007
Show More Cited By

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Demonstration

Conference

WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Erickson JYuzon JBonaci T(2022)What You Do Not Expect When You Are Expecting: Privacy Analysis of FemtechIEEE Transactions on Technology and Society10.1109/TTS.2022.31609283:2(121-131)Online publication date: Jun-2022
https://doi.org/10.1109/TTS.2022.3160928
Schufrin MReynolds SKuijper AKohlhammer J(2021)A Visualization Interface to Improve the Transparency of Collected Personal Data on the InternetIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302894627:2(1840-1849)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3028946
Schufrin MReynolds SKuijper AKohlhammer J(2020)A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet2020 IEEE Symposium on Visualization for Cyber Security (VizSec)10.1109/VizSec51108.2020.00007(1-10)Online publication date: Oct-2020
https://doi.org/10.1109/VizSec51108.2020.00007
Coleti TCorrea PFilgueiras LMorandini M(2020)TR-Model. A Metadata Profile Application for Personal Data TransparencyIEEE Access10.1109/ACCESS.2020.29885668(75184-75209)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2988566
Olteanu AHumbert MHuguenin KHubaux J(2019)The (Co-)Location Sharing GameProceedings on Privacy Enhancing Technologies10.2478/popets-2019-00172019:2(5-25)Online publication date: 4-May-2019
https://doi.org/10.2478/popets-2019-0017
Boutet AGambs SZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Inspect What Your Location History Reveals About YouProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357837(2861-2864)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357837
Primault VBoutet AMokhtar SBrunie L(2019)The Long Road to Computational Location Privacy: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2018.287395021:3(2772-2793)Online publication date: Nov-2020
https://doi.org/10.1109/COMST.2018.2873950
Murmann PFischer-Hubner S(2017)Tools for Achieving Usable Ex Post Transparency: A SurveyIEEE Access10.1109/ACCESS.2017.27655395(22965-22991)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2765539

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