Privacy-Preserving Smart Similarity Search Based on Simhash over Encrypted Data in Cloud Computing

Abstract

In recent years, due to the appealing features of cloud computing, more and more sensitive or private information has been outsourced onto the cloud. Although cloud computing provides convenience, privacy and security of data becomes a big concern. For protecting data privacy, it is desirable for the data owner to outsource sensitive data in encrypted form rather than in plain text. However, encrypted storage will hinder our legal access, e.g., searching function. To deal with this dilemma, a considerable number of searchable encryption schemes have been proposed in this field. However, almost all of existing schemes focus on keyword-based query rather than document-based query, which is a crucial requirement for real world application. In this paper, we propose a similarity search method for encrypted document based on simhash. Through our scheme, data users can find similar encrypted documents stored in cloud by submitting a query document. In order to scale well for large data sources, we build a triebased index to improve search efficiency in our solution. Through rigorous privacy analysis and experiment on realworld dataset, our scheme is secure and efficient.