Privacy in Internet Measurements Applied To WAN and Telematics (PIMAWAT)

Project Description

The PIMAWAT Project (Collaborative Research: IMR: MM-1-B: Privacy in Internet Measurements Applied To WAN and Telematics, supported by NSF CISE) will demonstrate new methods to provide data networking datasets that respect end-user privacy, but are still able to support new research in allow network protocols, security, privacy, and machine learning. Our insight is that most data today sent over the wide-area network (WAN) is encrypted, so our challenge is to demonstrate what data is encrypted, detect and scrub any remaining leaks, and finally anonymize the metadata (who talks to whom) before sharing data.

The intellectual focus of PIMAWAT will be to develop new methods to anonymize network traffic at scale, then use those new algorithms to evaluate potential data leakage, and demonstrate that real-world data sources can be scrubbed for sharing while respecting privacy.

The broader impacts of PIMAWAT will be to make it easier for researchers to collect and share network data through new tools and best-practices for privacy-respecting data scrubbing.

Support

PIMAWAT is supported by NSF/CISE as a CISE IMR award CNS-2319409.

People

  • John Heidemann, PI on this project, project leader and professor (USC/ISI)
  • Christos Papadopoulos, co-PI on this project, professor (University of Memphis) christos.papadopoulos (at) memphis.edu
  • Kicho Yu, PhD student (USC CS Dept. and ISI)

Publications

  • Basileal Imana, Zeyu Shen, Aleksandra Korolova and John Heidemann 2025. External Evaluation of Discrimination Mitigation Efforts in Meta’s Ad Delivery. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT) (Athens, Greece, Jun. 2025), to appear. [DOI] [PDF] [Dataset] Details
  • Basileal Imana, Zeyu Shen, Aleksandra Korolova and John Heidemann 2025. Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT) (Athens, Greece, Jun. 2025), to appear. [DOI] [PDF] [Dataset] Details
  • Asma Enayet and John Heidemann 2024. Durbin: Internet Outage Detection with Adaptive Passive Analysis. Technical Report arxiv:2411.17958. USC/Information Sciences Institute. [PDF] Details
  • Basileal Imana, Aleksandra Korolova and John Heidemann 2024. Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes. Technical Report 2410.23394v1. arXiv. [PDF] [Dataset] Details
  • ASM Rizvi, Tingshan Huang, Rasit Esrefoglu and John Heidemann 2024. Anycast Polarization in The Wild. Proceedings of the Passive and Active Measurement Workshop (Virtual Location, Mar. 2024). [PDF] [Dataset] Details
  • Giovane C. M. Moura, Marco Davids, Caspar Schutijser, Christian Hesselman, John Heidemann and Georgios Smaragdakis 2024. Deep Dive into NTP Pool’s Popularity and Mapping. ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems. 8, 1 (Mar. 2024), 30. [DOI] [PDF] Details

For related publications, please see the ANT publications web page.

Software

See also the see the ANT distribution web page.

Datasets

We make all datasets available through our dataset page.