Privacy-preserving Data Retrieval using Anonymous Query Authentication in Data Cloud Services

Recently, cloud computing became an essential part of most IT strategies. However, security and privacy issues are still the two main concerns that limit the widespread use of cloud services since the data is stored in unknown locations and retrieval of data (or part of it) may involve disclosure of sensitive data to unauthorized parties. Many techniques have been proposed to handle this problem, which is known as Privacy-Preserving Data Retrieval (PPDR). These techniques attempt to minimize the sensitive data that needs to be revealed. However, revealing any data to an unauthorized party breaks the security and privacy concepts and also may decrease the efficiency of the data retrieval. In this paper, different requirements are defined to satisfy a high level of security and privacy in a PPDR system. Moreover, a technique that uses anonymous query authentication and multi-server settings is proposed. The technique provides an efficient ranking-based data retrieval by using weighted Term Frequency-Inverse Document Frequency (TF-IDF) vectors. It also satisfies all of the defined security requirements that were completely unsatisfied by the techniques reported in the literature.