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 statistics) from owner data. These synopses then allow SPECIAL to estimate (compaction) sizes for secure operations (e.g., filter, join) and index encrypted data without extra privacy loss. Crucially, these estimates and indexes can be prepared before runtime, thereby facilitating efficient query planning and accurate cost estimations. Moreover, by using one-sided noise mechanisms and private upper bound techniques, SPECIAL ensures strict lossless processing for complex queries (e.g., multi-join). Through a comprehensive benchmark, we show that SPECIAL significantly outperforms cutting-edge SCAs, with up to 80X faster query times and over 900X smaller memory for complex queries. Moreover, it also achieves up to an 89X reduction in privacy loss under continual processing.

翻译：安全协作分析（SCA）能够在多个数据所有者之间处理分析型SQL查询，即使直接数据共享不可行。尽管对强隐私保护至关重要，但传统SCA中数据 oblivious 原语带来的巨大开销阻碍了其实际应用。近期允许在差分隐私（DP）下控制泄露的SCA变体在隐私与效率之间展现了更好的平衡，但仍面临重大挑战，例如潜在的无限隐私损失、次优查询规划及有损处理。为解决这些问题，我们提出SPECIAL——首个同时确保有限隐私损失、先进查询规划与无损处理的SCA系统。SPECIAL采用一种创新的概要辅助安全处理模型，该模型通过一次性隐私代价从所有者数据中获取私有概要（表统计信息）。这些概要使SPECIAL能够估计安全操作（如过滤、连接）的（压缩）规模，并对加密数据建立索引，且无需额外隐私损失。关键在于，这些估计与索引可在运行时之前准备，从而促进高效的查询规划与准确的成本估算。此外，通过采用单边噪声机制与私有上界技术，SPECIAL确保了复杂查询（如多表连接）的严格无损处理。通过全面基准测试，我们证明SPECIAL显著优于前沿SCA，复杂查询的查询速度提升高达80倍，内存占用减少超过900倍。同时，在持续处理下，其隐私损失也实现了高达89倍的降低。