An obfuscation framework for controlling value of information during sharing

Sensory information is characterized by its inherent quality and context-specific value. It is thus natural that the information provider would want to exercise control over the information shared based on her perception of the risk of possible misuse due to sharing and also depending on the consumer requirements. To attain this utility vs. risk trade-off, information is subjected to varying but deliberate quality modifying transformations which we term as obfuscation. In this paper, treating privacy as the primary motivation for information control, we highlight initial considerations of using feature sharing as an obfuscation mechanism to control the inferences possible from shared sensory data. We provide results from an activity tracking scenario to illustrate the use of feature selection in identifying the various trade-off points.

[1]  Kun Liu,et al.  Random projection-based multiplicative data perturbation for privacy preserving distributed data mining , 2006, IEEE Transactions on Knowledge and Data Engineering.

[2]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[3]  Mani B. Srivastava,et al.  Demystifying privacy in sensory data: A QoI based approach , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[4]  Reality Mining , 2014, Encyclopedia of Social Network Analysis and Mining.

[5]  H. Vincent Poor,et al.  A theory of utility and privacy of data sources , 2010, 2010 IEEE International Symposium on Information Theory.

[6]  Suman Nath,et al.  Privacy-aware regression modeling of participatory sensing data , 2010, SenSys '10.

[7]  Mani B. Srivastava,et al.  Building principles for a quality of information specification for sensor information , 2009, 2009 12th International Conference on Information Fusion.

[8]  Mani B. Srivastava,et al.  Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment , 2011, CHI.

[9]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[10]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.