Publication Type

Conference Proceeding Article

Publication Date

4-2012

Abstract

The text search queries in an enterprise can reveal the users' topic of interest, and in turn confidential staff or business information. To safeguard the enterprise from consequences arising from a disclosure of the query traces, it is desirable to obfuscate the true user intention from the search engine, without requiring it to be re-engineered. In this paper, we advocate a unique approach to profile the topics that are relevant to the user intention. Based on this approach, we introduce an $(\epsilon_1, \epsilon_2)$-privacy model that allows a user to stipulate that topics relevant to her intention at $\epsilon_1$ level should appear to any adversary to be innocuous at $\epsilon_2$ level. We then present a Top Priv algorithm to achieve the customized $(\epsilon_1, \epsilon_2)$-privacy requirement of individual users through injecting automatically formulated fake queries. The advantages of Top Priv over existing techniques are confirmed through benchmark queries on a real corpus, with experiment settings fashioned after an enterprise search application.

Keywords

Business information, Enterprise searches, Privacy models, Privacy requirements, Text search, User intention

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

ICDE 2012: IEEE 28th International Conference on Data Engineering, Arlington Virginia, 1-5 April 2012: Proceedings

First Page

1168

Last Page

1179

ISBN

9781467300421

Identifier

10.1109/ICDE.2012.43

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.ieeecomputersociety.org/10.1109/ICDE.2012.43

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