CRII: SaTC: Local Differential Privacy under Correlation

CRII：SaTC：相关下的本地差分隐私

• 批准号: 2245689
• 负责人: Yidan Hu
• 金额: $ 17.5万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245689&HistoricalAwards=false
• 关键词: CRII; SaTC; Local; Differential; Privacy

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.

随着数据已成为推动业务增长的燃料，越来越多的服务提供商从用户那里收集了大量数据，以获得洞察力，以获得更好的业务决策。 这些数据可能包含或揭示敏感的个人信息，并披露此类信息引起了公众的严重隐私问题。已经提出了各种本地差异私人（LDP）数据分析技术，以允许数据收集器从数据中获取有用的信息，同时确保用户的隐私。尽管如此，这些方法仍然在单个数据隐私和数据实用程序之间表现出固有的权衡，即单个数据贡献者的强大数据隐私是以减少数据收集器数据实用程序为代价的，这一直在阻碍其广泛的采用。该项目的新颖性在于利用通常存在于多属性数据中的相关性，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，并具有较大的隐私和公用事业交易。该项目的更广泛的意义和重要性包括服务提供商的新工具，以改善其收集和利用用户数据来推动其业务决策和增长的方式，同时确保在各种网络，移动和基于IoT的应用程序和服务的应用程序和服务中确保对个人用户的强大隐私保证以及具有隐私性的数据分析技术。该项目开发了新颖的新墨西哥低点技术，可以通过利用多属性数据中的相关性以及可以引入不同用户的随机扰动中的相关性来显着提高隐私和公用事业权衡。该项目将：（1）通过顺序的随机扰动开发新型的LDP技术，以改善数据实用性而不必牺牲隐私保证，以及（2）设计新颖的LDP技术，通过利用随机形成的数据贡献者的随机随机驱动来利用相关的随机驱动。该项目的发现将丰富保护隐私数据分析和增强隐私技术的科学知识。 从在线教程，演讲，出版物和软件工具包中，从项目中获得的见解和产出将公开共享。该项目将在课程开发中整合研究成果，并将通过本科和研究生指导为广泛的贡献，并向K-12和代表性不足的学生提供宣传。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查审查的审查标准来通过评估来通过评估来获得支持的。