CAREER: FormalDP: Formally Verified, Private, Accurate and Efficient Data Analysis

数据驱动的技术对社会产生了令人印象深刻的影响，但隐私问题限制了数据的使用和发布方式。差异隐私已经成为一个领先的概念，支持尊重隐私的高效和准确的数据分析。但是，设计和实现具有高实用性的高效差异隐私数据分析可能具有挑战性且容易出错。甚至隐私专家也发布了带有错误的代码或设计了不正确的算法。编程平台和正式验证工具可以帮助数据分析师设计没有错误的差异化私有数据分析。然而，目前的方法有两个局限性：第一，他们支持推理的隐私，但不对准确性和效率，这是数据分析的其他两个重要方面;第二，他们支持推理的理想化数据分析，但不对他们的有限计算机上的实现。这个项目的目标是克服这两个限制，通过开发新的形式验证技术和工具，支持正式推理相结合的隐私，准确性和效率保证的数据分析，及其实现。该项目的成果将有助于开发隐私保护技术的基本方法，通过改善和促进处理私人或敏感数据的更安全做法，使社会受益。此外，该项目将支持旨在培养学生对数据隐私及其实践具有全球视野的教育活动，以及旨在调查隐私政策和标准可能受到本研究结果影响的方式的外联活动。该提案的技术目标是验证技术和工具的理论和技术发展，这些技术和工具可以实现隐私数据分析的设计，准确、高效，并具有经过验证的实现。为实现这一目标，该项目将侧重于三个具体方向：第一，它将开发正式的验证技术，以正式的方式推理不同的私有数据分析的准确性;第二，它将开发资源分析技术，从计算时间和空间以及所需数据样本的数量方面推理数据分析的效率;第三，发展推理技术，以验证在有限电脑上执行不同保密算法的保密性，准确性和效率。这些技术将被实现为一个基于类型的验证框架，名为FormalDP，这将支持基于类型的正式推理相结合的隐私，准确性和效率的数据分析及其实现。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。

Data-driven technology is having an impressive impact on society but privacy concerns restrict the way data can be used and released. Differential privacy has emerged as a leading notion supporting efficient and accurate data analyses that respect privacy. But designing and implementing efficient differentially private data analyses with high utility can be challenging and error prone. Even privacy experts have released code with bugs or designed incorrect algorithms. Programming platforms and formal verification tools can assist data analysts in designing differentially private data analyses without bugs. However, the current approaches have two limitations: first, they support reasoning about privacy but not about accuracy and efficiency which are two other important aspects of data analyses; second, they support reasoning about idealized data analyses but not about their implementations on finite computers. The goal of this project is to overcome these two limitations by developing novel formal verification techniques and tools supporting formal reasoning combining privacy, accuracy, and efficiency guarantees for both data analyses, and their implementations. The results of this project will contribute to develop foundational methods for privacy-preserving technology which can benefit society by improving and promoting safer practices in handling private or sensitive data. Moreover, the project will support educational activities aimed at training students with a global view on data privacy and its practices, and outreach activities aimed at investigating ways in which privacy policies and standards can be impacted by the results of this research.The technical goal of this proposal is the theoretical and technological development of verification techniques and tools that can enable the design of data analyses that are private, accurate, efficient, and with verified implementations. To achieve this goal the project will focus on three concrete directions: first, it will develop formal verification techniques to reason in a formal way about the accuracy of differentially private data analyses; second, it will develop resource analysis techniques to reason about the efficiency of a data analysis in terms of computing time and space, and in terms of the number of needed data samples; third, it will develop reasoning techniques to verify the privacy, accuracy and efficiency of implementations of differentially private algorithms on finite computers. These techniques will be implement as a type-based verification framework, named FormalDP, which will thus support type-based formal reasoning combining privacy, accuracy and efficiency for data analyses and their implementations. The effectiveness of this approach will be assessed through experimental studies about the level of accuracy and efficiency that verified differentially private implementations can achieve.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.