K-Indistinguishable Data Access for Encrypted Key-Value Stores

Abstract

Key-value store is adopted by many applications due to its high performance in processing big data workloads. Recent research on secure cloud storage has shown that even if the data is encrypted, attackers can learn the sensitive information of data by launching access pattern attacks such as frequency analysis. For this issue, some schemes have been proposed to protect encrypted key-value stores against access pattern attacks. However, existing solutions protect access pattern information at the cost of large storage and bandwidth overhead, which is unacceptable for large-scale key-value stores. In this paper, we devise a K-indistinguishable frequency smoothing scheme for encrypted key-value stores, which can resist access pattern attacks launched by passive persistent adversaries with minimal storage and bandwidth overhead. Then, we propose a dynamic K-indistinguishable frequency smoothing scheme. It can efficiently adapt to the changes in access distribution while ensuring the K-indistinguishable security level and bandwidth efficiency. Finally, we formally analyze the security of our design. Extensive experiments demonstrate that our design achieves high throughput while minimizing storage and bandwidth overhead.