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

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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.