NeTS: RUI: Privacy by Design: Enabling Strong Privacy Guarantees with Open-Access Network Data

Important decisions about the Internet depend on data about the networks that help connect to everyday lives. However, these data are difficult to share privately and securely. Network and service providers are now able to collect records of how devices connect to wireless networks and how Internet traffic flows through their systems. These records can reveal how services are used by users. Sharing such data would help researchers and engineers make networks faster, more reliable, and fairer, but releasing them directly would put individual privacy at risk. This project develops mathematically rigorous privacy preserving techniques, so that network operators can share useful versions of their data without exposing sensitive information about any single person or organization. The project designs and analyzes a suite of differentially private mechanisms tailored to three canonical network data sharing scenarios. The first thrust focuses on single network providers that wish to share fine-grained workload traces with edge computing services while protecting individual users. The second thrust develops methods for releasing sequential mobility traces, such as device movements across campus wireless access points, using machine learning models combined with formally calibrated noise. The third thrust targets collaborative settings in which multiple Internet service providers may jointly compute statistics, such as heavy hitter IP prefixes and intersection counts, without revealing their own raw traffic or identifiers. Across these thrusts, the project plans to develop end-to-end algorithms, to address formal privacy guarantees, and to conduct empirical evaluations on real network datasets. The broader impacts of this project span education, open science, and technology transfer to the networking community. The work integrates privacy-preserving data analysis into undergraduate courses and year-long research experiences. The project releases open-source code, synthetic datasets, and teaching materials so that other instructors, researchers, and practitioners can learn how to apply differential privacy to network data. Outreach activities connect these ideas to kindergarten through grade 12 students and local community programs, illustrating how data-driven technologies intersect with questions of privacy and fairness. By demonstrating concrete, deployable methods for sharing network data safely, this project aims to lower barriers to collaboration between industry, academia, and policymakers on challenges in Internet measurement and security. 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. NSF Award ID: 2553435 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Vasanta Chaganti | Institution: Swarthmore College, SWARTHMORE, PA | Award Amount: $248,362 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553435 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553435.html