Query-based web search is an integral part of many people's daily activities. Most do not realize that their search history can be used to identify them (and their interests). In July 2006, AOL released an anonymized search query log of some 600K randomly selected users. While valuable as a research tool, the anonymization was insufficient: individuals were identified from the contents of the queries alone [2] . Government requests for such logs increases the concern. To address this problem, we propose a client-centered approach of plausibly deniable search. Each user query is substituted with a standard, closely-related query intended to fetch the desired results. In addition, a set of k-1 cover queries are issued; these have characteristics similar to the standard query but on unrelated topics. The system ensures that any of these k queries will produce the same set of k queries, giving k possible topics the user could have been searching for. We use a Latent Semantic Indexing (LSI) based approach to generate queries, and evaluate on the DMOZ [10] webpage collection to show effectiveness of the proposed approach.
Any subsection of k dummy queries will generate statistically indistinguishable outcomes to a corresponding unprecedented set of k queries REF .
Providing Privacy through Plausibly Deniable Search
SDM
Computer Science