Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
Private Intersection of Certified Sets
Financial Cryptography and Data Security
Proceedings of the 12th ACM international conference on Ubiquitous computing
Preserving location and absence privacy in geo-social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
The shy mayor: private badges in geosocial networks
ACNS'12 Proceedings of the 10th international conference on Applied Cryptography and Network Security
Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Mining User Mobility Features for Next Place Prediction in Location-Based Services
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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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.