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Secure collaborative analytics (SCA) enables the processing of analytical SQL queries across data from multiple owners, even when direct data sharing is not possible. While traditional SCA provides strong privacy through data-oblivious methods, the significant overhead has limited its practical use. Recent SCA variants that allow controlled leakages under differential privacy (DP) strike balance between privacy and efficiency but still face challenges like unbounded privacy loss, costly execution plan, 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 novelsynopsis-assisted secure processing model, where a one-time privacy cost is used to generate private synopses from owner data. These synopses enable SPECIAL to estimate compaction sizes for secure operations (e.g., filter, join) and index encrypted data without additional privacy loss. These estimates and indexes can be prepared before runtime, enabling efficient query planning and accurate cost estimations. By leveraging one-sided noise mechanisms and private upper bound techniques, SPECIAL guarantees lossless processing for complex queries (e.g., multi-join). Our comprehensive benchmarks demonstrate that SPECIAL outperforms state-of-the-art SCAs, with up to 80× faster query times, 900× smaller memory usage for complex queries, and up to 89× reduced privacy loss in continual processing. 

Most cloud service providers offer limited data privacy guarantees, discouraging clients from using them for managing their sensitive data. Cloud providers may use servers with Trusted Execution Environments (TEEs) to protect outsourced data, while supporting remote querying. However, TEEs may leak access patterns and allow communication volume attacks, enabling an honest-but-curious cloud provider to learn sensitive information. Oblivious algorithms can be used to completely hide data access patterns, but their high overhead could render them impractical. To alleviate the latter, the notion of Differential Obliviousness (DO) has been recently proposed. DO applies differential privacy (DP) on access patterns while hiding the communication volume of intermediate and final results; it does so by trading some level of privacy for efficiency. We present Doquet:DifferentiallyOblivious Range and JoinQueries with Private Data Structures, a framework for DO outsourced database systems. Doquet is the first approach that supports private data structures, indices, selection, foreign key join, many-to-many join, and their composition select-join in arealisticTEE setting, even when the accesses to the private memory can be eavesdropped on by the adversary. We prove that the algorithms in Doquet satisfy differential obliviousness. Furthermore, we implemented Doquet and tested it on a machine having a second generation of Intel SGX (TEE); the results show that Doquet offers up to an order of magnitude speedup in comparison with other fully oblivious and differentially oblivious approaches.