Publication Type
Journal Article
Version
publishedVersion
Publication Date
2-2010
Abstract
Users of online services are increasingly wary that their activities could disclose confidential information on their business or personal activities. It would be desirable for an online document service to perform text retrieval for users, while protecting the privacy of their activities. In this article, we introduce a privacy-preserving, similarity-based text retrieval scheme that (a) prevents the server from accurately reconstructing the term composition of queries and documents, and (b) anonymizes the search results from unauthorized observers. At the same time, our scheme preserves the relevance-ranking of the search server, and enables accounting of the number of documents that each user opens. The effectiveness of the scheme is verified empirically with two real text corpora.
Keywords
Privacy of search queries, Security in text retrieval, Singular value decomposition
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
ACM Transactions on Internet Technology
Volume
10
Issue
1
ISSN
1533-5399
Identifier
10.1145/1667067.1667071
Publisher
ACM
Citation
PANG, Hwee Hwa; SHEN, Jialie; and Krishnan, Ramayya.
Privacy-Preserving Similarity-Based Text Retrieval. (2010). ACM Transactions on Internet Technology. 10, (1),.
Available at: https://ink.library.smu.edu.sg/sis_research/220
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/1667067.1667071
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons
Comments
Article No.: 4