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Protecting User Privacy: Obfuscating Discriminative Spatio-Temporal Footprints

Published: 07 November 2017 Publication History

Abstract

In recent years, applications that collect and store location data have become ubiquitous, allowing users to engage in a variety of interactions with other users and services in their digital or physical vicinity. However, usage of these geolocation services put users at risk of serious privacy threats. For instance, state-of-the-art user-identification methods use geospatial trajectories derived from location based services to identify users at an alarmingly high accuracy. In this work, we address the problem of protecting user identities by presenting methods for obfuscating discriminative location data in users' profiles. We utilize data provided by the public Twitter API, collecting tweets with geolocation tags from a select group of prolific users in a 12-week time period. To minimize the amount of data obfuscated, we present two methods to identify the most discriminative tweets. The first solution is to use an Entropy-Maximizing Observation Function based on the number of tweets the user has posted and the number of people who have posted in that specific location. This ensures tweets by infrequent users in unique locations are changed first. The other solution is to use the identification algorithm to figure out what users can be identified and only change tweets from those users. For both methods, to perturb a tweet, we move it to a location with more tweets to mask the identity of the user. A thorough experimentation of other baseline approaches shows that our model exhibits a significant decrease in user identification accuracy while keeping the percentage of changed data at a minimum.

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cover image ACM Conferences
LocalRec'17: Proceedings of the 1st ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks
November 2017
45 pages
ISBN:9781450354998
DOI:10.1145/3148150
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 November 2017

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LocalRec'17 Paper Acceptance Rate 8 of 10 submissions, 80%;
Overall Acceptance Rate 17 of 26 submissions, 65%

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Cited By

View all
  • (2023)A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machinesCollective Intelligence10.1177/263391372311708252:2(263391372311708)Online publication date: 25-Apr-2023
  • (2023)CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact TracingIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.319510310:6(3385-3405)Online publication date: Dec-2023
  • (2019)Simulating Urban Patterns of LifeProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359106(576-579)Online publication date: 5-Nov-2019
  • (2019)Location-Based Social SimulationProceedings of the 16th International Symposium on Spatial and Temporal Databases10.1145/3340964.3340995(218-221)Online publication date: 19-Aug-2019
  • (2018)NTRU Implementation of Efficient Privacy-Preserving Location-Based Querying in VANETWireless Communications & Mobile Computing10.1155/2018/78239792018Online publication date: 3-May-2018

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