PIPages
Labs

Cori Faklaris

University of North Carolina at Charlotte, NC·Computer Science

When someone is harassed online or abused through a digital platform, there is no central place where that harm gets recorded. Systems exist for tracking large data breaches.

Fields

Computer Science

Social Science

Topics

privacy harms

privacy incident repository

online harassment

Grants
1 known grant, through Nov 2026

What they work on

  1. Gather privacy experts' opinions on how a centralized repository for tracking privacy harms, such as online harassment and cyber abuse, should be designed.
  2. Build a prototype privacy incident repository and collect feedback from experts who pilot it, to test whether a centralized system is feasible.
  3. Use the lessons from expert input and the pilot to write a plan for a full repository that helps researchers rank privacy harms, study affected populations and evaluate interventions.

More about the research

Other privacy violations that hurt ordinary users, such as online harassment and cyber abuse, are not tracked in any systematic way. Without that record, researchers find it hard to measure how much different platforms spread privacy harms, or how those platforms could be redesigned to be safer and more trustworthy.

Why it matters

A shared record of privacy incidents could help researchers and platform designers see which online harms, such as harassment and cyber abuse, are most common and who they affect most. That evidence could guide safer platform design and targeted interventions. Vulnerable users, who suffer most from digital privacy violations, would stand to benefit.

Known grants

From NIH and NSF research grants. Labs often have other funding we can't see.

Collaborative Research: CIRC: Planning-C: Building a Safer Cyberspace by Creating a Centralized Research Infrastructure to Track and Mitigate Privacy Harms

NSF · Jun 2024 – Nov 2026

Closest labs

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Create a standardized set of measures that human evaluators use to rate the quality and acceptability of images altered by adversarial machine learning, and publish a public repository of these ratings seeded with evaluations of existing anti-surveillance tools.

David Kotz

Dartmouth College, NH·Computer Science

Develop methods that let smart-home occupants discover, identify and locate Internet of Things (IoT) devices, including non-cooperative ones, and reveal the information flows those devices produce.

Dongxiao Zhu

Wayne State University, MI·Computer Science

Develop optimization algorithms that make large language model unlearning, removing specific data influences without retraining, more effective, robust and efficient.

The school

University of North Carolina at Charlotte: 9 labs on PI Pages, $95M a year on research, 196 research doctorates a year.

More about University of North Carolina at Charlotte

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