Anonymous Web transactions with Crowds
Communications of the ACM
Semantic Data Broadcast for a Mobile Environment Based on Dynamic and Adaptive Chunking
IEEE Transactions on Computers
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On Supporting Weakly-Connected Browsing in a Mobile Web Environment
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
XQuery implementation in a relational database system
VLDB '05 Proceedings of the 31st international conference on Very large data bases
BBQ: group-based querying in a ubiquitous environment
Proceedings of the 2006 ACM symposium on Applied computing
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Multi-resolution information transmission in mobile environments
Mobile Information Systems
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
P4A: A New Privacy Model for XML
Proceeedings of the 22nd annual IFIP WG 11.3 working conference on Data and Applications Security
Navigational path privacy protection: navigational path privacy protection
Proceedings of the 18th ACM conference on Information and knowledge management
| Hi-index | 0.00 |
In mobile and wireless environments, clients will request for information by submitting queries to the server, which delivers the required data via point-to-point connection or broadcast channels. The broadcast paradigm possesses the advantage of scalability. However, the broadcast data is vulnerable to eavesdropping and the adversary may be able to associate the requested data items and hence the original query with a specific client, a form of privacy threat that we would like to protect against. In this paper, we propose to protect the privacy of queries generated by individual clients by allowing them to hide themselves behind other clients. We assume a possibly untrustworthy server and make use of a trustworthy anonymizer for the queries. At the query anonymizer, user queries are clustered and consolidated into subsuming queries, to obfuscate the queries from being recovered. The consolidated queries are then expanded to improve the obfuscation effect, making it harder for the adversary to deduce the original queries. We define various performance metrics on the query privacy, by studying the ability that the adversary could crack the client queries, and propose algorithms to obfuscate a collection of user queries.