Optimal Compression for Encrypted Key-Value Store in Cloud Systems

Key-value store is adopted by many applications due to its high performance in processing big data workloads. With the increasing concern for privacy, some privacy-preserving key-value storage systems have been proposed. A remarkable solution is to group key-value pairs into packs and then compress and encrypt each pack separately. The selection of pack size is important for key-value storage systems because it affects both the storage cost in the cloud and the bandwidth cost for data retrieval. However, existing data packing strategies do not consider the trade-off between them. In this paper, we study the optimal compression problem for encrypted key-value stores, aiming to minimize the overall cost of data outsourcing. To solve this problem, we devise an optimal pack size computation scheme, which considers both storage and bandwidth costs. Then, we propose a privacy-preserving key-value storage system. It balances the impact caused by encryption and compression without compromising system performance. Meanwhile, it supports dynamic updates and rich types of queries. Finally, we formally analyze the security of our design. Performance evaluations demonstrate that our proposed pack size computation scheme can minimize the overall cost of data outsourcing, and the designed key-value storage system is feasible in practice.

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