Home Location Protection in Mobile Social Networks: A Community Based Method (Short Paper)

Location privacy has drawn much attention among mobile social network users, as the geo-location information can be used by the adversaries to launch localization attacks which focus on finding people’s sensitive locations such as home and office place. In this paper, we propose a community based information sharing scheme to help the users to protect their home locations. First, we study the existing home location prediction algorithms and conclude that they are all mainly based on the spatial and temporal features of the check-in data. Then we design the community based information sharing scheme which aggregates the check-ins of all community members, thus change the overall spatial and temporal features. Finally, our simulation results validate that our proposed scheme greatly reduces the home location predication accuracy and therefore can protect the user’s privacy effectively.

[1]  Kyumin Lee,et al.  You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.

[2]  Der-Jiunn Deng,et al.  Toward trustworthy crowdsourcing in the social internet of things , 2016, IEEE Wireless Communications.

[3]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[4]  K. K. Ramakrishnan,et al.  Mining checkins from location-sharing services for client-independent IP geolocation , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[5]  Song Guo,et al.  Crowdsourcing-Based Content-Centric Network: A Social Perspective , 2017, IEEE Network.

[6]  Jeffrey Nichols,et al.  Home Location Identification of Twitter Users , 2014, TIST.

[7]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Yulong Gu,et al.  We Know Where You Are: Home Location Identification in Location-Based Social Networks , 2016, 2016 25th International Conference on Computer Communication and Networks (ICCCN).

[9]  Rui Wang,et al.  Towards social user profiling: unified and discriminative influence model for inferring home locations , 2012, KDD.

[10]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

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

[12]  Jun Hu,et al.  Effective location identification from microblogs , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[13]  Cecilia Mascolo,et al.  Socio-Spatial Properties of Online Location-Based Social Networks , 2011, ICWSM.

[14]  Virgílio A. F. Almeida,et al.  We know where you live: privacy characterization of foursquare behavior , 2012, UbiComp.

[15]  Jean-Yves Le Boudec,et al.  Quantifying Location Privacy , 2011, 2011 IEEE Symposium on Security and Privacy.

[16]  Jeffrey Nichols,et al.  Where Is This Tweet From? Inferring Home Locations of Twitter Users , 2012, ICWSM.

[17]  Fahad Bin Muhaya,et al.  Estimating Twitter User Location Using Social Interactions--A Content Based Approach , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.