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

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.

[1]  Joachim Hagenauer,et al.  A Viterbi algorithm with soft-decision outputs and its applications , 1989, IEEE Global Telecommunications Conference, 1989, and Exhibition. 'Communications Technology for the 1990s and Beyond.

[2]  Radu Sion,et al.  The Shy Mayor: Private Badges in GeoSocial Networks , 2012, ACNS.

[3]  Cecilia Mascolo,et al.  Mining User Mobility Features for Next Place Prediction in Location-Based Services , 2012, 2012 IEEE 12th International Conference on Data Mining.

[4]  Ken Barker,et al.  A Data Privacy Taxonomy , 2009, BNCOD.

[5]  D. A. Field.  Laplacian smoothing and Delaunay triangulations , 1988 .

[6]  Dan Boneh,et al.  Location Privacy via Private Proximity Testing , 2011, NDSS.

[7]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[8]  Mikko Alava,et al.  Patterns, Entropy, and Predictability of Human Mobility and Life , 2012, PloS one.

[9]  Keiichi Tokuda,et al.  Speech parameter generation algorithms for HMM-based speech synthesis , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[10]  Jan Camenisch,et al.  Private Intersection of Certified Sets , 2009, Financial Cryptography.

[11]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[12]  Mohamed F. Mokbel,et al.  Location-based and preference-aware recommendation using sparse geo-social networking data , 2012, SIGSPATIAL/GIS.

[13]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[14]  Prateek Mittal,et al.  DECENT: A decentralized architecture for enforcing privacy in online social networks , 2011, 2012 IEEE International Conference on Pervasive Computing and Communications Workshops.

[15]  Ramakrishnan Srikant,et al.  Hippocratic Databases , 2002, VLDB.

[16]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[17]  Roni Rosenfeld,et al.  Learning Hidden Markov Model Structure for Information Extraction , 1999 .

[18]  Christian S. Jensen,et al.  Preserving location and absence privacy in geo-social networks , 2010, CIKM '10.

[19]  Md. Moniruzzaman,et al.  A study of privacy policy enforcement in access control models , 2010, 2010 13th International Conference on Computer and Information Technology (ICCIT).

[20]  Md. Moniruzzaman,et al.  Delegation of access rights in a privacy preserving access control model , 2011, 2011 Ninth Annual International Conference on Privacy, Security and Trust.

[21]  John Krumm,et al.  Exploring end user preferences for location obfuscation, location-based services, and the value of location , 2010, UbiComp.

[22]  Daniel Gatica-Perez,et al.  Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns , 2012, MUM.