SaTC: CORE: Medium: Secure outsourced analytics in untrusted clouds

该项目设计和开发了Secrecy+，这是一个使用安全多方计算(MPC)的新型数据分析系统，使数据持有者能够在可证明和可配置的安全保证下对他们的集体私有数据执行关系分析。该项目的核心创新包括一个用于外包关系型MPC的原则性优化框架和一个并行安全操作符的库，这些操作符可以扩展到比当前最先进的数据集大得多的数据集。保密+目标使用的情况是，多个数据持有者愿意将他们的私人数据贡献给联合分析(例如，为了利润、社会公益或改善服务)，前提是数据仍然孤立于不受信任或未经授权的实体。该研究基于两个案例研究：(I)基于云的移动健康数据安全分析，以及(Ii)数据中心日志的安全跨站点分析。该项目涉及三组任务，涉及密码学、数据库查询评估和分布式系统。首先，调查人员为跨不同MPC协议和物理部署的安全操作定义了统一的成本模型。其次，他们开发了一个支持并行查询执行的关系型MPC查询处理器，并采用了在保持密码协议的完全安全保证的同时降低MPC成本的优化。第三，调查人员设计和实施高级用户界面和工具，以实现与现有云基础设施的无缝集成。项目成果有可能从根本上改变组织、研究人员和政策制定者根据数据隐私法规使用私有数据集的方式，并为云中的新市场铺平道路。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project designs and develops Secrecy+, a novel data analytics system that uses secure multi-party computation (MPC) and enables data holders to perform relational analytics on their collective private data with provable and configurable security guarantees. The project's core novelties include a principled optimization framework for outsourced relational MPC and a library of parallel secure operators that scale to much larger datasets than the current state-of-the-art. Secrecy+ targets use cases where multiple data holders are willing to contribute their private data towards a joint analysis (e.g., for profit, social good, or improved services), provided that the data remain siloed from untrusted or unauthorized entities. The research is grounded in two case studies: (i) secure cloud-based analytics on mobile health data, and (ii) secure cross-site analytics on datacenter logs.The project involves three sets of tasks that span the areas of cryptography, database query evaluation, and distributed systems. First, the investigators define a unified cost model for secure operations across different MPC protocols and physical deployments. Second, they develop a relational MPC query processor that supports parallel query execution and employs optimizations that reduce MPC costs while retaining the full security guarantees of the cryptographic protocols. Third, the investigators design and implement high-level user interfaces and tools for seamless integration with existing cloud infrastructure. Project results have the potential to fundamentally change how private datasets are used by organizations, researchers, and policy makers in accordance with data privacy regulations and can pave the way for new marketplaces in the cloud.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.