Reasoning about obfuscated private information: who have lied and how to lie

In ubiquitous environments, context sharing among agents should be made privacy-conscious. Privacy preferences are generally specified to govern the context exchanging among agents. Besides who has rights to see what information, a user's privacy preference could also designate who has rights to have what obfuscated information. By obfuscation, people could present their private information in a coarser granularity, or simply in a falsified manner, depending on the specific situations. Nevertheless, people cannot randomly obfuscate their private information because by reasoning the recipients could detect the obfuscation. In this paper, we present a Bayesian network-based method to reason about the obfuscation. On the one hand, it can be used to find if the received information has been obfuscated, and if so, what the true information could be; on the other hand, it can be used to help the obfuscators reasonably obfuscate their private information.

[1]  Erwin Pesch,et al.  Constraint propagation techniques for the disjunctive scheduling problem , 2000, Artif. Intell..

[2]  Karl N. Levitt,et al.  Data level inference detection in database systems , 1998, Proceedings. 11th IEEE Computer Security Foundations Workshop (Cat. No.98TB100238).

[3]  Harry Chen,et al.  An ontology for context-aware pervasive computing environments , 2003, The Knowledge Engineering Review.

[4]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[5]  Hans-Werner Gellersen,et al.  Beyond Prototypes: Challenges in Deploying Ubiquitous Systems , 2002, IEEE Pervasive Comput..

[6]  Josep Domingo-Ferrer,et al.  Inference Control in Statistical Databases , 2002, Lecture Notes in Computer Science.

[7]  Joachim Biskup,et al.  Controlled Query Evaluation for Known Policies by Combining Lying and Refusal , 2004, Annals of Mathematics and Artificial Intelligence.

[8]  Valérie Issarny,et al.  Context-aware service discovery in heterogeneous networks , 2005, Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks.

[9]  James A. Landay,et al.  An architecture for privacy-sensitive ubiquitous computing , 2004, MobiSys '04.

[10]  Jessica Staddon Dynamic inference control , 2003, DMKD '03.

[11]  Joachim Biskup,et al.  Controlled Query Evaluation for Known Policies by Combining Lying and Refusal , 2002, FoIKS.

[12]  Lars Kulik,et al.  A Formal Model of Obfuscation and Negotiation for Location Privacy , 2005, Pervasive.

[13]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[14]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[15]  Fabien L. Gandon,et al.  Semantic web technologies to reconcile privacy and context awareness , 2003, Journal of Web Semantics.

[16]  Stephen A. Cook,et al.  The complexity of theorem-proving procedures , 1971, STOC.

[17]  Dorothy E. Denning,et al.  Inference Controls for Statistical Databases , 1983, Computer.

[18]  Joachim Biskup,et al.  Lying versus refusal for known potential secrets , 2001, Data Knowl. Eng..

[19]  Hung Keng Pung,et al.  A BAYESIAN APPROACH FOR DEALING WITH UNCERTAIN CONTEXTS , 2004 .

[20]  Joachim Biskup For unknown secrecies refusal is better than lying , 1999, Data Knowl. Eng..