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.
Privacy of search queries, Security in text retrieval, Singular value decomposition
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
ACM Transactions on Internet Technology
PANG, Hwee Hwa; SHEN, Jialie; and Krishnan, Ramayya.
Privacy-Preserving Similarity-Based Text Retrieval. (2010). ACM Transactions on Internet Technology. 10, (1),. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/220
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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.