New Techniques for Private Stream Searching

Abstract

A system for private stream searching, introduced by Ostrovsky and Skeith, allows a client to provide an untrusted server with an encrypted search query. The server uses the query on a stream of documents and returns the matching documents to the client while learning nothing about the nature of the query. We present a new scheme for conducting private keyword search on streaming data which requires O ( m ) server to client communication complexity to return the content of the matching documents, where m is an upper bound on the size of the documents. The required storage on the server conducting the search is also O ( m ). The previous best scheme for private stream searching was shown to have O ( m log m ) communication and storage complexity. Our solution employs a novel construction in which the user reconstructs the matching files by solving a system of linear equations. This allows the matching documents to be stored in a compact buffer rather than relying on redundancies to avoid collisions in the storage buffer as in previous work. This technique requires a small amount of metadata to be returned in addition to the documents; for this the original scheme of Ostrovsky and Skeith may be employed with O ( m log m ) communication and storage complexity. We also present an alternative method for returning the necessary metadata based on a unique encrypted Bloom filter construction. This method requires O ( m log( t / m )) communication and storage complexity, where t is the number of documents in the stream. In this article we describe our scheme, prove it secure, analyze its asymptotic performance, and describe a number of extensions. We also provide an experimental analysis of its scalability in practice. Specifically, we consider its performance in the demanding scenario of providing a privacy preserving version of the Google News Alerts service.