Answering Why-not Questions on Top $k$ Queries with Privacy Protection

Outsourcing data services to the public cloud will help data owners save administrative costs, while it may bring privacy concerns. To guarantee the confidentiality of sensitive data against the untrusted cloud and unauthorized users, one way is to have the cloud perform the query processing with encrypted query requests over outsourced encrypted data. However, it is of great challenge to support various types of query processing, especially those of answering why-not questions, under data encryption settings without sensitive information leakage. In this paper, we define and solve the problem of answering why-not questions on top-$k$ queries with privacy protection. To address the problem, we propose two Secure Why-Not top $k$ Query (SWN $k$ Q) processing approaches. In the basic approach, we adopt the Paillier cryptosystem to guarantee the semantic security of data and queries and propose two new secure protocols to support computations on ciphertext in SWN $k$ Q processing. Then, we propose a secure weighing space generation method for obtaining the best approximate refined query. To solve the efficiency problem of the basic approach, we further propose an optimized approach, in which dominance-based secure data pruning and early stopping conditions are presented to improve the query efficiency by pruning searching space in the retrieval of refined queries. Thorough analysis shows the security and computational complexity of our approaches, and extensive experimental results on real datasets further demonstrate the query performance of our approaches.