Rank-Based Inference Over Web Databases

In recent years, there has been much research in the adoption of Ranked Retrieval model (in addition to the Boolean retrieval model) in structured databases, especially those in a client-server environ-ment (e.g., web databases). With this model, a search query re-turns top-k tuples according to not just exact matches of selection conditions, but a suitable ranking function. While much research has gone into the design of ranking functions and the efficient pro-cessing of top-k queries, this paper studies a novel problem on the privacy implications of database ranking. The motivation is a novel yet serious privacy leakage we found on real-world web databases which is caused by the ranking func-tion design. Many such databases feature private attributes- e.g., a social network allows users to specify certain attributes as only visible to him/herself, but not to others. While these websites gen-erally respect the privacy settings by not directly displaying private attribute values in search query answers, many of them nevertheless take into account such private attributes in the ranking function de-sign. The conventional belief might be that tuple ranks alone are not enough to reveal the private attribute values. Our investigation, however, shows that this is not the case in reality. To address the problem, we introduce a taxonomy of the prob-lem space with two dimensions, (1) the type of query interface and (2) the capability of adversaries. For each subspace, we develop a novel technique which either guarantees the successful inference of private attributes, or does so for a significant portion of real-world tuples. We demonstrate the effectiveness and efficiency of our techniques through theoretical analysis, extensive experiments over real-world datasets, as well as successful online attacks over websites with tens to hundreds of millions of users- e.g., Amazon Goodreads and Renren.com