Journal
SIGMOD '21: PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
Volume -, Issue -, Pages 1892-1905Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3448016.3457306
Keywords
Update pattern; Encrypted database; Differential privacy
Categories
Funding
- National Science Foundation [2016393, 2029853]
- DARPA
- SPAWAR [N66001-15-C-4067]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [2029853] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [2016393] Funding Source: National Science Foundation
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This paper explores privacy-preserving update strategies for secure outsourced growing databases, introducing a novel framework called DP-Sync to address the issue of information leakage in existing techniques. Extensive empirical evaluations on real-world datasets demonstrate the practical feasibility of DP-Sync in terms of performance and accuracy.
In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow append-only data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to securely outsource database computation, existing techniques do not consider the information that can be leaked via update patterns. To address this problem, we design a novel secure outsourced database framework for growing data, DP-Sync, which interoperate with a large class of existing encrypted databases and supports efficient updates while providing differentially-private guarantees for any single update. We demonstrate DP-Sync's practical feasibility in terms of performance and accuracy with extensive empirical evaluations on real world datasets.
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