Privacy and Efficiency Tradeoffs for Multiword Top K Search with Linear Additive Rank Scoring

This paper proposes a privacy-aware ranking scheme with linear additive scoring for efficient top 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, and server-side query processing deals with feature vector sparsity through optional feature matching and enables result filtering 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.