SPECIAL: Synopsis Assisted Secure Collaborative Analytics
Chenghong Wang, Lina Qiu, Johes Bater, Yukui Luo

TL;DR
SPECIAL introduces a novel secure collaborative analytics system that ensures bounded privacy loss, enables lossless query processing, and significantly improves efficiency and privacy preservation over existing solutions.
Contribution
It is the first SCA system to combine bounded privacy loss, advanced query planning, and lossless processing using a synopsis-assisted model.
Findings
Up to 80X faster query execution compared to existing SCA systems.
Over 900X reduction in memory usage for complex queries.
Up to 89X decrease in privacy loss during continual processing.
Abstract
Secure collaborative analytics (SCA) enable the processing of analytical SQL queries across multiple owners' data, even when direct data sharing is not feasible. Although essential for strong privacy, the large overhead from data-oblivious primitives in traditional SCA has hindered its practical adoption. Recent SCA variants that permit controlled leakages under differential privacy (DP) show a better balance between privacy and efficiency. However, they still face significant challenges, such as potentially unbounded privacy loss, suboptimal query planning, and lossy processing. To address these challenges, we introduce SPECIAL, the first SCA system that simultaneously ensures bounded privacy loss, advanced query planning, and lossless processing. SPECIAL employs a novel synopsis-assisted secure processing model, where a one-time privacy cost is spent to acquire private synopses (table…
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Taxonomy
TopicsData Quality and Management · Semantic Web and Ontologies
