Collaborative Research: SaTC: CORE: Medium: Quicksilver: A Write-oriented, Private, Outsourced Database Management System

企业、非营利组织和其他组织越来越多地将其数据库需求外包给第三方云提供商。与本地部署相比，云提供了许多竞争优势，包括更低的成本、高可用性、前所未有的可扩展性以及易于部署和维护。与此同时，混合交易和分析处理（HTAP）系统的兴起，通过在单一平台上支持交易和分析功能，为满足实时数据驱动的商业智能需求提供了额外的希望。然而，隐私、法律的和政治约束要求组织最小化并正式量化泄露到其组织边界之外的信息。 虽然在使用加密数据库和多方计算在不受信任的硬件上构建查询应答系统方面有很多工作，但这些努力几乎都没有处理数据库更新，支持交叉操作的事务，或者在确保强大隐私保证的同时提供故障恢复。这项工作将利用四个研究领域之间的协同作用-数据库，遗忘RAM，安全多方计算和差异隐私。 Quicksilver将使查询敏感数据成为可能，同时将数据库操作外包给不受信任的云提供商，这项工作将对医疗保健，联邦统计机构（如美国人口普查局），金融和教育产生直接影响。 研究人员将把这项研究整合到一个全面的教育、传播和推广计划中，这将导致（a）新的研究生、本科生和研究生课程，使用开源材料，（B）指导研究生--特别是女性和URMs --在学习期间获得成功的技术，（c）为高中教师提供数据科学开源课程计划，以及（d）为联邦机构的雇员举办关于这些主题的课程。在这个项目中，研究人员将设计，实施和评估在不受信任的第三方平台上进行数据处理的原则性技术。 研究人员将首先设计和实现有效的协议，用于使用密码学，差分隐私和系统技术更新数据库。 然后，他们将创建和评估隐私保护并发控制的算法，以便Quicksilver的事务将提供具有原子性，一致性，隔离性和持久性（ACID）的交叉事务，这是数据库系统的黄金标准。 此外，这项工作将导致在安全计算下的交易恢复协议时，个别云节点失败的中间事务。 这项研究将揭示在不经意的查询处理，安全多方计算，和差分隐私algorithm.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Businesses, non-profits, and other organizations are increasingly outsourcing their database needs to third-party cloud providers. The cloud offers many competitive advantages over on-premises deployments including lower costs, high availability, unprecedented scalability, and ease of deployment and maintenance. At the same time, the rise of a hybrid transaction and analytics processing (HTAP) systems presents an additional promise to address the need for real-time data-driven business intelligence by supporting both transactional as well as analytical functions in a single platform. Privacy, legal, and political constraints, however, require organizations to minimize and formally quantify the information leaked outside their organizational boundaries. While there has been much work on building query answering systems on untrusted hardware using encrypted databases and multiparty computation, almost none of these efforts handle database updates, support transactions with interleaving operations, or offer recovery from failures while ensuring strong privacy guarantees. This work will exploit synergies among four areas of research -- databases, oblivious RAMs, secure multiparty computation, and differential privacy. Quicksilver will make it possible to query sensitive data while outsourcing database operations to untrusted cloud providers and this work will have immediate implications for healthcare, federal statistical agencies such as the US Census Bureau, finance, and education. The investigators will integrate this research into a comprehensive education, dissemination and outreach plan that will result in (a) new graduate and undergraduate and graduate courses with open-source materials, (b) the mentoring of graduate students -- especially women and URMs -- on techniques for thriving during their studies, (c) open-source lesson plans for high school teachers on data science, and (d) courses for employees of federal agencies on these topics. In this project, the investigators will design, implement, and evaluate principled techniques for data processing on untrusted third-party platforms. The investigators will first design and implement efficient protocols for updating databases using techniques in cryptography, differential privacy, and systems. They will then create and evaluate algorithms for privacy-preserving concurrency control so that Quicksilver’s transactions will offer interleaving transactions with atomicity, consistency, isolation, and durability (ACID), the gold standard of database systems. In addition, this work will result in protocols for transaction recovery under secure computation when individual cloud nodes fail mid-transaction. This research will reveal novel techniques in oblivious query processing, secure multiparty computation, and differentially-private algorithms.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.