Obfuscating the topical intention in enterprise text search

Hwee Hwa Pang; Xiaokui Xiao; Jialie Shen

doi:10.1109/ICDE.2012.43

Obfuscating the topical intention in enterprise text search

Hwee Hwa Pang, Xiaokui Xiao, Jialie Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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 (ε ₁, ε ₂)-privacy model that allows a user to stipulate that topics relevant to her intention at ε ₁ level should appear to any adversary to be innocuous at ε ₂ level. We then present a Top Priv algorithm to achieve the customized (ε ₁, ε 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.

Original language	English
Title of host publication	2012 IEEE 28th International Conference on Data Engineering
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1168-1179
Number of pages	12
ISBN (Electronic)	978-0-7695-4747-3
ISBN (Print)	978-1-4673-0042-1
DOIs	https://doi.org/10.1109/ICDE.2012.43
Publication status	Published - 30 Jul 2012
Externally published	Yes
Event	IEEE 28th International Conference on Data Engineering, ICDE 2012 - Arlington, VA, United States Duration: 1 Apr 2012 → 5 Apr 2012

Conference

Conference	IEEE 28th International Conference on Data Engineering, ICDE 2012
Country/Territory	United States
City	Arlington, VA
Period	1/04/12 → 5/04/12

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10.1109/ICDE.2012.43

Cite this

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title = "Obfuscating the topical intention in enterprise text search",

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 (ε 1, ε 2)-privacy model that allows a user to stipulate that topics relevant to her intention at ε 1 level should appear to any adversary to be innocuous at ε 2 level. We then present a Top Priv algorithm to achieve the customized (ε 1, ε 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.",

author = "Pang, {Hwee Hwa} and Xiaokui Xiao and Jialie Shen",

year = "2012",

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doi = "10.1109/ICDE.2012.43",

language = "English",

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booktitle = "2012 IEEE 28th International Conference on Data Engineering",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "IEEE 28th International Conference on Data Engineering, ICDE 2012 ; Conference date: 01-04-2012 Through 05-04-2012",

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Pang, HH, Xiao, X & Shen, J 2012, Obfuscating the topical intention in enterprise text search. in 2012 IEEE 28th International Conference on Data Engineering. IEEE, Institute of Electrical and Electronics Engineers, pp. 1168-1179, IEEE 28th International Conference on Data Engineering, ICDE 2012, Arlington, VA, United States, 1/04/12. https://doi.org/10.1109/ICDE.2012.43

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T1 - Obfuscating the topical intention in enterprise text search

AU - Pang, Hwee Hwa

AU - Xiao, Xiaokui

AU - Shen, Jialie

PY - 2012/7/30

Y1 - 2012/7/30

N2 - 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 (ε 1, ε 2)-privacy model that allows a user to stipulate that topics relevant to her intention at ε 1 level should appear to any adversary to be innocuous at ε 2 level. We then present a Top Priv algorithm to achieve the customized (ε 1, ε 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.

AB - 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 (ε 1, ε 2)-privacy model that allows a user to stipulate that topics relevant to her intention at ε 1 level should appear to any adversary to be innocuous at ε 2 level. We then present a Top Priv algorithm to achieve the customized (ε 1, ε 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.

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PB - IEEE, Institute of Electrical and Electronics Engineers

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Y2 - 1 April 2012 through 5 April 2012

ER -

Obfuscating the topical intention in enterprise text search

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