EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Multi-regulation computation

这个跨学科的项目调查是否现有的加密技术分析孤立的数据comport与参与者的法律的限制数据披露。安全多方计算（MPC）是一种来自密码学的技术，其允许每个具有敏感信息的多个参与者共同分析他们的数据而无需共享数据。一些公司、政府和非营利组织已经采用MPC来向人们提供对社会有益的信息（例如，计算全市范围的工资差距，同时隐藏个人工资），由于原始数据的敏感性，这些数据可能是不可能或几乎不可能学习的。MPC非常适合分析受保护的教育、医疗保健或司法数据;然而，MPC在这些领域的部署很少，部分原因是难以评估MPC技术是否足以满足关于包括个人身份信息的数据披露的法律的规定。该项目的核心问题是开发MPC技术，同时为敏感输入数据提供加密和法律的保护。该项目分为三个阶段，每个阶段的网络安全和法律的研究人员之间都有双向的知识流动。首先，调查人员正在确定使用MPC算法分析的信息受到多个州、联邦和国际法规的约束的用例，这些法规对隐私和数据共享进行了限制，他们还检查了信息使用的相关法律的约束。其次，研究人员正在设计MPC协议，除了通常的加密安全概念，即各方的观点可以被模拟之外，还保证在合法的信任和共谋假设下不可能重建任何受法律保护的信息。第三，研究人员正在通过分析法律的要求和潜在的政策反对意见，严格审查新开发的协议是否提供了足够的保护，允许各方使用受监管的数据，而不会引发对数据使用的额外法律的限制。这三个阶段的过程将通过向法律的社区证明MPC可以在不引发额外的繁重的法律的义务或政策问题的情况下改进数据分析，为MPC的更广泛采用铺平道路。该奖项反映了NSF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。

This interdisciplinary project investigates whether existing cryptographic techniques for analyzing siloed data comport with participants' legal restrictions on data disclosure. Secure multi-party computation (MPC) is a technique from cryptography that allows several participants, each with sensitive information, to analyze their data collectively without ever sharing it. Several companies, governments, and non-profit organizations have adopted MPC to provide people with socially beneficial information (e.g., computing the city-wide wage gap while hiding individual salaries) that may otherwise be impossible or near-impossible to learn due to the sensitivity of the raw data. MPC is well-suited toward analyses of protected education, healthcare, or judicial data; however, deployments of MPC in these areas are scarce, in part due to the difficulty of assessing whether MPC technology suffices to meet legal regulations on the disclosure of data that includes personally identifiable information. The core question for this project is to develop MPC technology that simultaneously provides cryptographic and legal protection of sensitive input data.This project has three phases, with a bidirectional flow of knowledge among cybersecurity and legal researchers in each phase. First, the investigators are identifying use cases in which information to be analyzed using MPC algorithms is subject to multiple state, federal, and international regulations that impose privacy restrictions and limit data sharing, and they examine the relevant legal constraints on information use. Second, the investigators are designing MPC protocols that, in addition to the usual cryptographic security notion that each party's view can be simulated, also guarantee the impossibility of reconstructing any legally-protected information under legally-compliant assumptions of trust and collusion. Third, the investigators are examining critically whether the newly-developed protocols provide sufficient protection to allow parties to use regulated data without triggering additional legal constraints on data use, by analyzing the legal requirements and potential policy objections. This three-phase process will pave the way for greater adoption of MPC by demonstrating to the legal community that MPC can improve data analysis without triggering additional burdensome legal obligations or policy concerns.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.