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FPF Unveils Paper on State Data Minimization Trends
Today, the Future of Privacy Forum (FPF) published a new paper—Data Minimization’s Substantive Turn: Key Questions & Operational Challenges Posed by New State Privacy Legislation. Data minimization is a bedrock principle of privacy and data protection law, with origins in the Fair Information Practice Principles (FIPPs) and the Privacy Act of 1974. At a high […]
The Curse of Dimensionality: De-identification Challenges in the Sharing of Highly Dimensional Datasets
[…] and privacy budget management. Errors in implementation, such as underestimating sensitivity or mismanaging the privacy budget across multiple queries (due to composition rules), can silently undermine the promised privacy guarantees. Defining the “privacy unit” (e.g., user, query, session) appropriately is critical; misclassification can lead to unintended disclosures. Auditing DP implementations for correctness is also […]
FPF Launches Major Initiative to Study Economic and Policy Implications of AgeTech
FPF and University of Arizona Eller College of Management Awarded Grant by Alfred P. Sloan Foundation to Address Privacy Implications, and Data Uses of Technologies Aimed at Aging At Home The Future of Privacy Forum (FPF) — a global non-profit focused on data protection, AI and emerging technologies–has been awarded a grant from the Alfred […]
What to Expect in Global Privacy in 2025
Next year, in 2026, we will celebrate a decade after the adoption of the GDPR, a law with an unprecedented regulatory impact around the world, from California to Brazil, across the African continent, to India, to China, and everywhere in between. The field of data protection and privacy has become undeniably global, with GDPR-inspired laws […]
Five Big Questions (and Zero Predictions) for the U.S. State Privacy Landscape in 2025
[…] minimization provisions are also elements of recent sectoral laws including the Washington State My Health My Data Act, the New York Child Data Protection Act, and the Virginia Child Data Privacy Amendment. Taken together, these frameworks portend a new trend toward substantive data minimization standards; however, their statutory requirements vary in subtle but consequential […]
Asia-Pacific
The Asia-Pacific Team FPF APAC is led by Josh Lee Kok Thong. Since its inception, FPF APAC has committed itself to the furthering of FPF’s global mission in the region, including to foster greater understanding, convergence, and interoperability of data protection and emerging technology regulation in the Asia-Pacific. Featured Asia-Pacific Focused Work Resources Global Offices With […]
Manipulative and Deceptive Design: New Challenges in Immersive Environments
With help from Selin Fidan, Beth Do, Daniel Berrick, and Angela Guo Immersive technologies like spatial computing, gaming, and extended reality (XR) offer exciting ways to experience and engage with the world. However, interfaces for immersive technologies that further blur the lines between the physical and the virtual may also open the door to new, […]
FPF Develops Checklist & Guide to Help Schools Vet AI Tools for Legal Compliance
FPF’s Youth and Education team has developed a checklist and accompanying policy brief to help schools vet generative AI tools for compliance with student privacy laws. Vetting Generative AI Tools for Use in Schools is a crucial resource as the use of generative AI tools continues to increase in educational settings. It’s critical for school […]
The Old Line State Does Something New on Privacy
On April 6, the Maryland Senate concurred with House amendments to SB 541, the Maryland Online Data Privacy Act (MODPA), sending the bill to Governor Moore for signature. If enacted, MODPA could be a paradigm-shifting addition to the state privacy law landscape. While recent state comprehensive privacy laws generally have added to the existing landscape […]
AI Audits, Equity Awareness in Data Privacy Methods, and Facial Recognition Technologies are Major Topics During This Year’s Privacy Papers for Policymakers Events
[…] think deeply about predictions rather than ban them altogether. Hideyuki Matsumi and Professor Daniel Solove In the evening’s final presentation, Robin Staab and Mislav Balunovic (ETH Zurich SRI Lab) discussed their paper, Beyond Memorization: Violating Privacy Via Inference with Large Language Models, with Professor Alicia Solow-Niederman (George Washington University Law School). Their paper, co-written […]