CAREER: Efficient Query Processing for Private Data Federations

几乎生活的所有领域，包括医学、政府和商业，都有许多独立机构以前所未有的速度记录着有关它们的数据。为了从这些支离破碎的数据集中获得洞察力，数据科学家通常会建立一个数据联盟，其中多个自治数据库被联合起来作为单个查询引擎。在许多情况下，由于隐私问题和监管要求，这是具有挑战性的。这个项目研究了一个私有数据联盟，它在多个源的组合秘密记录上运行查询（用SQL），这样查询执行中显示的唯一信息就是可以从其输出中推断出来的信息。联盟使用安全的多方计算来保护其输入。这些加密协议在数据提供程序之间不受影响地运行，因此它们的执行独立于查询的秘密输入。该项目的研究成果，包括开源的私有数据联合原型，可以使数据库用户在没有专门培训的情况下，从破碎的数据集中获得可操作的见解。该项目识别、形式化并利用各种机会，以有效地运行私有数据联合查询，同时维护其隐私保证。它使用关系模型和安全计算之间的协同作用为遗忘数据库操作符创建新算法，从而最大限度地减少它们对重量级加密协议的使用。研究人员设计并评估了一个代数，该代数利用关系模型的属性（如键定义和完整性约束）来绑定查询中间结果的基数，从而降低其安全电路的复杂性。该项目利用多家医院用于临床数据研究的电子健康记录评估该技术的实际影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Almost all domains of life, including medicine, government, and business, have data recorded on them at an unprecedented rate by many independent parties. To realize insights from these fractured datasets, data scientists often set up a data federation in which multiple autonomous databases are united to appear as a single engine for querying. In many settings this is challenging due to privacy concerns and regulatory requirements. This project investigates a private data federation that runs queries (in SQL) over the combined secret records of multiple sources such that the only information revealed from a query's execution is that which can be deduced from its output. The federation uses secure multi-party computation to protect its inputs. These cryptographic protocols run obliviously among the data providers such that their execution is independent of a query's secret inputs. The findings from the project, which include the open-source private data federation prototype, could empower database users to gain actionable insights from fractured datasets without specialized training. This project identifies, formalizes, and exploits opportunities to run private data federation queries efficiently while upholding their privacy guarantees. It uses synergies between the relational model and secure computation to create new algorithms for oblivious database operators that minimize their use of heavyweight cryptographic protocols. The investigators design and evaluate an algebra that leverages properties of the relational model, such as key definitions and integrity constraints, to bound the cardinality of a query's intermediate results thereby reducing the complexity of its secure circuits. The project evaluates the real-world impact of this technology with electronic health records from multiple hospitals for clinical data research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.