Which cloak dresses you best?: comparing location cloaking methods for mobile users

Location cloaking methods enable the protection of private location data. Different temporal and spatial approaches to cloak a specific user location (e.g., k-anonymity) have been suggested. Besides the research focusing on functionality, little work has been done on how cloaking methods should be presented to the user. In practice common location referencing services force the user to either accept or deny exact positioning. Therefore, users are not enabled to regulate private location information on a granular level. To improve the usage of location cloaking methods and foster location privacy protection, we conducted a user study (N = 24) comparing different visualized cloaking methods. The results of our lab study revealed a preference for visualizations using already known and well understood real world entities. Thus, the usage of simple and real world concepts can contribute to the application of cloaking methods and subsequently to location privacy protection.

[1]  Lorrie Faith Cranor,et al.  Privacy manipulation and acclimation in a location sharing application , 2013, UbiComp.

[2]  Elisa Bertino,et al.  The PROBE Framework for the Personalized Cloaking of Private Locations , 2010, Trans. Data Priv..

[3]  Panos Kalnis,et al.  Location Diversity: Enhanced Privacy Protection in Location Based Services , 2009, LoCA.

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

[5]  Jason Hong,et al.  sometimes less is more: multi-perspective exploration of disclosure abstractions in location-aware social mobile applications , 2010 .

[6]  Pierangela Samarati,et al.  Location privacy in pervasive computing , 2008 .

[7]  Mikkel Baun Kjærgaard,et al.  The SITA principle for location privacy — Conceptual model and architecture , 2013, 2013 International Conference on Privacy and Security in Mobile Systems (PRISMS).

[8]  Helen Nissenbaum,et al.  Privacy in Context - Technology, Policy, and the Integrity of Social Life , 2009 .

[9]  Marco Gruteser,et al.  USENIX Association , 1992 .

[10]  Chi-Yin Chow,et al.  Privacy in location-based services: a system architecture perspective , 2009, SIGSPACIAL.

[11]  Daniel P. Siewiorek,et al.  Understanding how visual representations of location feeds affect end-user privacy concerns , 2011, UbiComp '11.

[12]  Matthew Smith,et al.  Selective cloaking: Need-to-know for location-based apps , 2013, 2013 Eleventh Annual Conference on Privacy, Security and Trust.

[13]  Louise Barkhuus The mismeasurement of privacy: using contextual integrity to reconsider privacy in HCI , 2012, CHI.

[14]  Mary Beth Rosson,et al.  Measuring Mobile Users' Concerns for Information Privacy , 2012, ICIS.

[15]  Kim Sheehan,et al.  Toward a Typology of Internet Users and Online Privacy Concerns , 2002, Inf. Soc..

[16]  Sören Preibusch,et al.  Guide to measuring privacy concern: Review of survey and observational instruments , 2013, Int. J. Hum. Comput. Stud..

[17]  Lars Kulik,et al.  Location privacy and location-aware computing , 2006 .

[18]  Hua Lu,et al.  SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[19]  H. Jeff Smith,et al.  Information Privacy: Measuring Individuals' Concerns About Organizational Practices , 1996, MIS Q..