PSS: Achieving high-efficiency and privacy-preserving similarity search in multiple clouds

To preserve privacy, sensitive data in cloud computing needs to be encrypted before outsourcing, which obstacles data utilization based on plaintext search. Thus there spring up several secure schemes which enable encrypted cloud-data search. However, these single-cloud-supported search schemes would suffer from service failure, inefficient application, and privacy problem when they are applied to the multi-cloud applications. In this paper, we propose a Privacy-preserving Similarity Search scheme termed PSS. We exploit the n-grams method and counting bloom filters to define and compute the keyword-order. Based on this order, all indexing elements could be organized in a Chord-ring to support multi-cloud similarity search with high efficiency. Moreover, we extend the prefix technique to obtain strong privacy protection. Finally, a proof for the non-adaptive semantic security and the chosen-keyword attack resistance of PSS is given. Extensive experiments on real-world dataset further confirm the high efficacy and efficiency of PSS scheme.