Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
For unknown secrecies refusal is better than lying
Data & Knowledge Engineering
Constraint propogation techniques for the disjunctive scheduling problem
Artificial Intelligence
Lying versus refusal for known potential secrets
Data Engineering
Understanding and Using Context
Personal and Ubiquitous Computing
Beyond Prototypes: Challenges in Deploying Ubiquitous Systems
IEEE Pervasive Computing
Data Level Inference Detection in Database Systems
CSFW '98 Proceedings of the 11th IEEE workshop on Computer Security Foundations
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Controlled Query Evaluation for Known Policies by Combining Lying and Refusal
Annals of Mathematics and Artificial Intelligence
An architecture for privacy-sensitive ubiquitous computing
Proceedings of the 2nd international conference on Mobile systems, applications, and services
An ontology for context-aware pervasive computing environments
The Knowledge Engineering Review
Context-Aware Service Discovery in Heterogeneous Networks
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
Learning Bayesian Networks
A formal model of obfuscation and negotiation for location privacy
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
| Hi-index | 0.00 |
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.