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Redeem with privacy (RWP): privacy protecting framework for geo-social commerce

Published: 04 November 2013 Publication History

Abstract

Users are encouraged to check in to commercial places in Geo-social networks (GSNs) by offering discounts on purchase. These promotions are commonly known as deals. When a user checks in, GSNs share the check-in record with the merchant. However, these applications, in most cases, do not explain how the merchants handle check-in histories nor do they take liability for any information misuse in this type of services. In practice, a dishonest merchant may share check-in histories with third parties or use them to track users' location. It may cause privacy breaches like robbery, discovery of sensitive information by combining check-in histories with other data, disclosure of visits to sensitive places, etc. In this work, we investigate privacy issues arising from the deal redemptions in GSNs. We propose a privacy framework, called Redeem with Privacy (RwP), to address the risks. RwP works by releasing only the minimum information necessary to carry out the commerce to the merchants. The framework is also equipped with a recommendation engine that helps users to redeem deals in such a way that their next visit will be less predictable to the merchants. Experimental results show that inference attacks will have low accuracy when users check in using the framework's recommendation.

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Cited By

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  • (2017)Inferring Time when People Visit a Location Using Social Network Data2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2017.00-52(838-841)Online publication date: Dec-2017
  • (2014)Disclosing too much? Situational factors affecting information disclosure in social commerce environmentElectronic Commerce Research and Applications10.1016/j.elerap.2014.06.00713:5(305-319)Online publication date: 1-Sep-2014

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Published In

cover image ACM Conferences
WPES '13: Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
November 2013
306 pages
ISBN:9781450324854
DOI:10.1145/2517840
  • General Chair:
  • Ahmad-Reza Sadeghi,
  • Program Chair:
  • Sara Foresti
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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Association for Computing Machinery

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Publication History

Published: 04 November 2013

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Author Tags

  1. geo-social networks
  2. privacy
  3. time series analysis

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WPES '13 Paper Acceptance Rate 30 of 103 submissions, 29%;
Overall Acceptance Rate 106 of 355 submissions, 30%

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View all
  • (2017)Inferring Time when People Visit a Location Using Social Network Data2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2017.00-52(838-841)Online publication date: Dec-2017
  • (2014)Disclosing too much? Situational factors affecting information disclosure in social commerce environmentElectronic Commerce Research and Applications10.1016/j.elerap.2014.06.00713:5(305-319)Online publication date: 1-Sep-2014

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