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[…] companies applied differential privacy techniques to retain information from personal data while complying with legal requirements to delete it. By anonymizing data before deletion, differential privacy allows businesses to generate summaries, trends, and patterns that do not compromise individual privacy. The participants discussed this new technique through the lens of data deletion, and whether […]

On April 25, 2025, the Future of Privacy Forum and the Mozilla Foundation co-hosted a Privacy Enhancing Technologies (PETs) Workshop in Washington, DC, convening industry, academia, and civil society experts to explore practical applications of PETs. The workshop featured two leading-edge use cases: Mastercard’s cross-border fraud detection system using Fully Homomorphic Encryption (FHE), and Oblivious’s […]

[…] organizations extract and retain aggregate-level information while ensuring that individual-level data is permanently erased and regulatory obligations are met. By anonymizing data before deletion, Differential Privacy allows businesses to generate summaries, trends, and patterns that do not compromise individual privacy. When data reaches its scheduled deletion, the organization processes it into Differentially Private flat […]

[…] and deletes the source data once processed. Differential Privacy’s mathematical guarantees ensure that no individual can be reidentified, enabling organizations to model risk, refine premiums, and maintain business continuity without compromising Privacy. The Research Coordination Network (RCN) for Privacy-Preserving Data Sharing and Analytics is supported by the U.S. National Science Foundation under Award #2413978 […]

Mastercard’s use of Fully Homomorphic Encryption (FHE) demonstrates how Privacy Enhancing Technologies (PETs) can support fraud detection across borders without compromising sensitive data. In this use case, Mastercard collaborated with Singapore’s Infocomm Media Development Authority to pilot a system that allows encrypted International Bank Account Numbers (IBANs) to be checked for fraud risk without revealing […]

[…] The homepage displays the latest activity from not only your current working group communities, but all FPF communities you are able to join. In the center activity feed, you will see recent community discussions, announcements, library resources, and event details. Use the filter feature in the right corner of the activity feed to switch […]

This Preview highlights key findings from FPF’s forthcoming report “2025 U.S. State AI Legislation: An Examination of State Approaches to AI,” which provides a data-driven snapshot of enacted and key AI bills affecting the private sector, organizes activity into distinct approaches, and helps stakeholders understand emerging trends and obligations. As AI technologies rapidly integrate […]

[…] prominence to certain results, based on general consumer data or specific customer behavioral data. This could potentially include changing the prominence of given products based on their price. This resource distinguishes between these different pricing strategies in order to help lawmakers, businesses, and consumers better understand how these different practices work. View the resource here

[…] that often look different from one another, create different benefits for consumers and companies, and carry different risks. Distinguishing and disaggregating these pricing strategies will help policymakers, businesses, and consumers better understand how the relevant technologies and practices work, and better analyze the relevant benefits and risks. The chart below describes the major different […]