Privacy and Efficiency Tradeoffs for Multiword TopKSearch with Linear Additive Rank Scoring
Daniel Agun, Jinjin Shao, Shiyu Ji, Stefano Tessaro, Tao Yang · 2018
This paper proposes a private ranking scheme with linear additive scoring for efficient top K keyword search on modest-sized cloud datasets. This scheme strikes for tradeoffs between privacy and efficiency by proposing single-round client-server collaboration with server-side partial ranking based on blinded feature weights with random masks. Client-side preprocessing includes query decomposition with chunked postings to facilitate earlier range intersection and fast access of server-side key-value stores. Server-side query processing deals with feature vector sparsity through optional feature matching and enables result filtering with query-dependent chunk-wide random masks for queries that yield too many matched documents. This paper provides details on indexing and run-time conjunctive query processing and presents an evaluation that assesses the accuracy, efficiency, and privacy tradeoffs of this scheme through five datasets with various sizes.