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Numerous studies have underscored the significant privacy risks associatedwith various leakage patterns in encrypted data stores. Most existing systemsthat conceal leakage either (1) incur substantial overheads, (2) focus onspecific subsets of leakage patterns, or (3) apply the same security notionacross various workloads, thereby impeding the attainment of fine-tunedprivacy-efficiency trade-offs. In light of various detrimental leakagepatterns, this paper starts with an investigation into which specific leakagepatterns require our focus respectively in the contexts of key-value,range-query, and dynamic workloads. Subsequently, we introduce new securitynotions tailored to the specific privacy requirements of these workloads.Accordingly, we present, SWAT, an efficient construction that progressivelyenables these workloads, while provably mitigating system-wide leakage via asuite of algorithms with tunable privacy-efficiency trade-offs. We conductedextensive experiments and compiled a detailed result analysis, showing theefficiency of our solution. SWAT is about $10.6\times$ slower than anencryption-only data store that reveals various leakage patterns and is$31.6\times$ faster than a trivially zero-leakage solution. Meanwhile, theperformance of SWAT remains highly competitive compared to other designs thatmitigate specific types of leakage.

SWAT: A System-Wide Approach to Tunable Leakage Mitigation in Encrypted Data Stores