CAREER: A Utility Aware Framework for Privately Sharing Individual Level Data

可以收集和共享个人层面的数据，以利于广泛的研究、研究和探索性应用。然而，大多数隐私保护数据共享解决方案侧重于定义明确的聚合分析，而没有考虑应用程序的实用目标。此外，现有的解决方案可能在解决特定于应用的隐私需求方面受到限制，导致隐私保护过强或不足。该项目通过将应用的隐私需求和效用目标整合到一个优化框架中来开发新的隐私保护数据共享解决方案。该框架与各种严格的隐私模型兼容。更重要的是，它允许应用程序定制隐私机制的结构以及细粒度的输出实用程序。该项目展示了定制框架以支持真实世界应用程序的可行性，例如，在健康和行为领域。此外，为了便于在广泛的领域采用，该项目通过数据中的统计交互来估计效用损失，并开发出计算高效的技术来解决大规模问题。此外，该项目研究特定领域的隐私风险，例如在快速增长的应用程序中，以制定框架中不断变化的隐私需求。该项目的结果将有利于各种领域的研究和应用，这些领域依赖于个人贡献的数据，例如行为和健康研究。该项目还将研究和教育与一些相互关联的活动相结合，包括课程开发、学生指导、跨学科合作和K-12外展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Individual level data can be collected and shared to benefit a wide range of research studies and exploratory applications. However, most privacy protecting data sharing solutions focus on well-defined aggregate analysis and do not take into account the application’s utility goals. Furthermore, current solutions may be limited in addressing the application-specific privacy needs, resulting in overly strong or inadequate privacy protection.This project develops novel privacy protecting data sharing solutions by incorporating an application’s privacy needs and utility goals in one optimization framework. The framework is compatible with a variety of rigorous privacy models. More importantly, it allows the application to customize the privacy mechanism’s structure as well as fine-grained output utility. The project shows the feasibility of customizing the framework to support real-world applications, e.g., in health and behavioral domains. Furthermore, to facilitate adoption in a wide range of domains, the project estimates the utility loss via statistical interactions in the data and develops computationally efficient techniques to solve large scale problems. Moreover, the project studies domain-specific privacy risks, e.g., in rapidly growing applications, to formulate the evolving privacy needs in the framework. The results of the project will benefit research studies and applications in a variety of domains that rely on individually contributed data, such as, behavioral and health studies. The project also integrates research and education with a number of interconnected activities, including curriculum development, student mentoring, interdisciplinary collaboration, and K-12 outreach.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.