Searchable Encryption for Outsourced Data Analytics

Two sets of privacy requirements need to be fulfilled when a company’s accounting data is audited by an external party: the company needs to safeguard its data, while the auditors do not want to reveal their investigation methods. This problem is usually addressed by physically isolating data and auditors during the course of an audit. This approach however no longer works when auditing is performed remotely. In this paper we present an efficient construction for a searchable encryption scheme for outsourcing data analytics. In this scheme the data owner needs to encrypt his data only once and ship it in encrypted form to the data analyst. The data analyst can then perform a series of queries for which he must ask the data owner for help in translating the constants in the queries. Our searchable encryption scheme extends previous work by the ability to re-use query results as search tokens (query-result reusability) and the ability to perform range queries. It is efficient with O(log n) work for a range query and is semantically secure relying only on Diffie-Hellman assumptions (in the random oracle model).