CAREER: SaTC: Towards Machine-learnable Enhancing Framework for Local Differential Privacy

职业：SaTC：面向本地差异隐私的机器学习增强框架

• 批准号: 2238680
• 负责人: Wenhai Sun
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238680&HistoricalAwards=false
• 关键词: CAREER; SaTC; Towards; Machine; learnable

The prevalence of data-centric applications demands collecting and analyzing the information that may contain sensitive data of users. While local differential privacy (LDP) can quantifiably control information leakage to protect user privacy, it is challenging to tackle a dynamic threat landscape where the attacker can manipulate the analytical results by exploiting the characteristics of LDP design. The project’s novelties are to leverage machine intelligence along with other enabling techniques to understand the complex interplay between LDP privacy, security, and utility, and develop a machine-learnable LDP enhancing framework to address their tension. The success of the project will pave the way toward a future where ubiquitous machine intelligence can understand and attend to user privacy, security, and utility demands in various challenging scenarios. The project’s broader significance and importance are broadening the participation of women and underrepresented students in STEM; encouraging interdisciplinary, cross-sector partnership, and technology transformation and incubation; and promoting diversity-and-equity-aware technology development. This CAREER project aims to develop a novel AI-assisted privacy-enhancing framework that can better address the tension between LDP security, utility, and privacy. Generalizable theories and principles will be produced to guide the developed machine agent to sense the deployment environment and learn optimal responses to the observed adversarial actions, expected utility, and privacy goals. The agent will also be self-explainable for the actions it takes and can interact with both users and developers, thereby improving algorithmic transparency and accountability. In addition, human stakeholders will get involved in the growing cycle of the system, allowing it to evolve over time. The project also aims to conduct research-informed educational activities that will strengthen cybersecurity-related education and mentoring programs in the home department while enhancing cybersecurity workforce training at the university level and beyond. Meanwhile, the project seeks to promote STEM careers by engaging in outreach activities with K-12 students with diverse backgrounds.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.

以数据为中心的应用程序的盛行需要收集和分析可能包含用户敏感数据的信息，虽然本地差分隐私（LDP）可以量化地控制信息泄漏以保护用户隐私，但应对攻击者所处的动态威胁环境具有挑战性。可以通过利用 LDP 设计的特征来操纵分析结果。该项目的新颖之处在于利用机器智能以及其他支持技术来理解 LDP 隐私、安全性和实用性之间的复杂相互作用，并开发一个可机器学习的 LDP 增强框架。解决他们的该项目的成功将为未来铺平道路，让无处不在的机器智能能够理解并满足各种具有挑战性的场景中的用户隐私、安全和实用需求。该项目的更广泛意义和重要性正在扩大女性和代表性不足的群体的参与。 STEM 的学生；鼓励跨学科、跨部门的合作以及技术转型和孵化；并促进具有多样性和公平意识的技术发展。可以更好地解决 LDP 安全性、效用和隐私之间的紧张关系，从而指导开发的机器代理感知部署环境并学习对观察到的对抗行为、预期效用和隐私目标的最佳响应。它所采取的行动也将是不言自明的，并且可以与用户和开发人员进行交互，从而提高算法的透明度和问责制。此外，人类利益相关者将参与系统的成长周期，使其随着时间的推移而发展。该项目还旨在开展以研究为基础的教育活动，将加强内政部的网络安全相关教育和指导计划，同时加强大学及其他层面的网络安全劳动力培训。同时，该项目旨在通过与 K-12 学生开展外展活动来促进 STEM 职业发展。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。