Embellishing Text Search Queries to Protect User Privacy

Users of text search engines are increasingly wary that their activities may disclose confidential information about their business or personal profiles. It would be desirable for a search engine to perform document retrieval for users while protecting their intent. In this paper, we identify the privacy risks arising from semantically related search terms within a query, and from recurring highspecificity query terms in a search session. To counter the risks, we propose a solution for a similarity text retrieval system to offer anonymity and plausible deniability for the query terms, and hence the user intent, without degrading the system’s precision-recall performance. The solution comprises a mechanism that embellishes each user query with decoy terms that exhibit similar specificity spread as the genuine terms, but point to plausible alternative topics. We also provide an accompanying retrieval scheme that enables the search engine to compute the encrypted document relevance scores from only the genuine search terms, yet remain oblivious to their distinction from the decoys. Empirical evaluation results are presented to substantiate the effectiveness of our solution. 1

Authenticating the Query Results of Text Search Engines

The number of successful attacks on the Internet shows that it is very difficult to guarantee the security of online search engines. A breached server that is not detected in time may return incorrect results to the users. To prevent that, we introduce a methodology for generating an integrity proof for each search result. Our solution is targeted at search engines that perform similarity-based document retrieval, and utilize an inverted list implementation (as most search engines do). We formulate the properties that define a correct result, map the task of processing a text search query to adaptations of existing threshold-based algorithms, and devise an authentication scheme for checking the validity of a result. Finally, we confirm the efficiency and practicality of our solution through an empirical evaluation with real documents and benchmark queries. 1