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普遍性需要收集和分析可能包含用户敏感数据的信息。虽然当地的差异隐私（LDP）可以量化控制信息泄漏以保护用户隐私，但要解决动态威胁格局的挑战，攻击者可以通过利用LDP设计的特征来操纵分析结果。该项目的新颖性是利用机器智能以及其他促成技术来了解LDP隐私，安全性和实用程序之间的复杂相互作用，并开发机器可行的LDP增强框架以解决其紧张局势。该项目的成功将为未来的机器智能可以理解并关注用户隐私，安全性和公用事业在各种挑战场景中的需求铺平道路。该项目的重要性和重要性正在扩大妇女和代表性不足的学生的参与。鼓励跨学科，跨部门的伙伴关系以及技术转型和孵化；并促进多样性和平等感知的技术发展。该职业项目旨在开发一个新颖的AI辅助隐私增强框架，可以更好地解决自由顾展安全，公用事业和隐私之间的紧张关系。将制定可概括的理论和原则来指导开发的机器代理，以感知部署环境并了解对观察到的对抗行动，预期效用和隐私目标的最佳响应。代理商也可以为其采取的动作提供自我解释，并可以与用户和开发人员进行交互，从而提高算法透明度和问责制。此外，人类利益相关者将参与系统的增长周期，从而随着时间的推移而发展。该项目还旨在进行研究知识的教育活动，以加强内政部与网络安全有关的教育和心理计划，同时在大学及以后加强网络安全劳动力培训。意思是，该项目试图通过与具有潜水员的K-12学生进行外展活动来促进STEM职业。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响评估标准来通过评估来获得的支持。