Fast Privacy-Preserving Top-k Queries Using Secret Sharing

Over the past several years a lot of research has focused on distributed top-k computation. In this work we are interested in the following privacy-preserving distributed top-k problem. A set of parties hold private lists of key-value pairs and want to find and disclose the fe key-value pairs with largest aggregate values without revealing any other information. We use secure multiparty computation (MPC) techniques to solve this problem and design two MPC protocols, PPTK and PPTKS, putting emphasis on their efficiency. PPTK uses a hash table to condense a possibly large and sparse space of keys and to probabilistically estimate the aggregate values of the top-k keys. PPTKS uses multiple hash tables, i.e., sketches, to improve the estimation accuracy of PPTK. We evaluate our protocols using real traffic traces and show that they accurately and efficiently aggregate distributions of IP addresses and port numbers to find the globally most frequent IP addresses and port numbers.