CRII: SaTC: Local Differential Privacy under Correlation

随着数据成为推动业务增长的动力，越来越多的服务提供商从用户那里收集大量数据，以获得更好的业务决策。此类数据可能包含或披露敏感的个人信息，而披露此类信息会引起公众对隐私的重大关注。已经提出了各种本地差分私有（LDP）数据分析技术，以允许数据收集器从数据中获得有用的信息，同时确保用户的隐私。尽管如此，这些方法在个人数据隐私和数据效用之间表现出一种内在的权衡，即，个人数据贡献者的强大数据隐私是以降低数据收集器的数据效用为代价的，这一直阻碍着它们的广泛采用。这个项目的新颖之处在于利用多属性数据中普遍存在的相关性，例如，一个人的年龄和工资，以及新的相关随机扰动技术来开发有效的LDP技术，大大改善了隐私和效用权衡。该项目的更广泛意义和重要性包括为服务提供商提供新工具，以改进他们收集和利用用户数据的方式，以推动其业务决策和增长，同时确保对个人用户的强大隐私保障，以及在各种网络、移动和基于物联网的应用和服务中保护隐私的数据分析技术。该项目开发了新颖的LDP技术，通过利用多属性数据中的相关性以及可以引入不同用户随机扰动的相关性，显著改善了隐私和效用权衡。该项目将：(1)通过顺序随机扰动为相关多属性数据开发新的LDP技术，以提高数据效用，同时不牺牲隐私保证；(2)通过利用随机形成的数据贡献者群体之间的相关随机扰动，设计新的LDP技术，改善隐私和效用权衡。该项目的研究结果将丰富保护隐私的数据分析和增强隐私的技术的科学知识。从项目中获得的见解和产出将通过在线教程、讲座、出版物和软件工具包公开分享。该项目将把研究成果纳入课程开发，并将通过对本科生和研究生的指导，以及对K-12和代表性不足的学生的推广，做出广泛贡献。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。

As data has become the fuel that drives business growth, an increasing number of service providers collect large volumes of data from users to gain insights for better business decision-making. Such data may contain or reveal sensitive personal information, and disclosing such information raises significant privacy concerns among the general public. Various Local Differential Private (LDP) data analysis techniques have been proposed to allow a data collector to gain helpful information from the data while ensuring users' privacy. Still, these methods exhibit an inherent trade-off between individual data privacy and data utility, i.e., strong data privacy for individual data contributors comes at the cost of reduced data utility for the data collector, which has been hindering their broad adoption. This project's novelties lie in exploiting the correlation that commonly exists in multi-attribute data, e.g., a person's age and salary, and new correlated random perturbation techniques to develop effective LDP techniques with much-improved privacy and utility tradeoff. The project's broader significance and importance include new tools for service providers to improve how they collect and utilize user data to drive their business decisions and growth while ensuring strong privacy guarantees to individual users as well as privacy-preserving data analysis techniques in various web, mobile, and IoT-based applications and services. This project develops novel LDP techniques to significantly improve the privacy and utility tradeoff by exploiting the correlation in multi-attribute data and the correlation that can be introduced into different users' random perturbations. The project will: (1) develop novel LDP techniques for correlated multi-attribute data via sequential random perturbation for improving data utility without sacrificing privacy guarantee, and (2) design novel LDP techniques with improved privacy and utility tradeoffs by exploiting correlated random perturbation among randomly formed groups of data contributors. The findings from this project will enrich the scientific knowledge of privacy-preserving data analysis and privacy-enhancing technologies. Insights gained from and outputs of the project will be made publicly shared through online tutorials, talks, publications, and software toolkits. The project will integrate research outputs in curriculum development, and will contribute broadly through undergraduate and graduate mentoring, and outreach to K-12 and underrepresented students.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.