FPF to Present First-Ever Research Data Stewardship Award
FPF is requesting nominations for its Award for Research Data Stewardship.
Award-Winning Paper: "Privacy's Constitutional Moment and the Limits of Data Protection"
For the tenth year, FPF’s annual Privacy Papers for Policymakers program is presenting to lawmakers and regulators award-winning research representing a diversity of perspectives. Among the papers to be honored at an event at the Hart Senate Office Building on February 6, 2020 is Privacy’s Constitutional Moment and the Limits of Data Protection by Woodrow […]
FPF Receives Grant To Design Ethical Review Process for Research Access to Corporate Data
Future of Privacy Forum (FPF) has received a grant to create an independent party of experts for an ethical review process that can provide trusted vetting of corporate-academic research projects. FPF will establish a pool of respected reviewers to operate as a standalone, on-demand review board to evaluate research uses of personal data and create a set of transparent policies and processes to be applied to such reviews.
New White Paper Explores Privacy and Security Risk to Machine Learning Systems
FPF and Immuta Examine Approaches That Can Limit Informational or Behavioral Harms WASHINGTON, D.C. – September 20, 2019 – The Future of Privacy Forum (FPF) released a white paper, WARNING SIGNS: The Future of Privacy and Security in an Age of Machine Learning, exploring how machine learning systems can be exposed to new privacy and […]
10th Annual Privacy Papers for Policymakers – Send Us Your Work!
The 10th Annual Privacy Papers for Policymakers awards have been announced. Register here to attend the event on February 6, 2020. We will open the submissions process for next year’s awards in fall 2020. Have you conducted privacy-related research that policymakers should know about? If so, we can help you get it in front of […]
School Safety Report Neglects Privacy Concerns
Yesterday, the Federal Commission on School Safety released a report detailing its conclusions, after holding a series of meetings and hearings in the wake of school shootings. Nearly every aspect of the Commission’s report focuses on sharing data and, thus, has privacy implications for students, teachers, and the public.
New Resource on FERPA's Health and Safety Emergency
The Future of Privacy Forum has released a new guide, Disclosing Student Information During School Emergencies: A Primer for Schools, which offers four best practices for information disclosure and answers five frequently asked questions about FERPA’s requirements for sharing information during health or safety emergencies. Read more about this guide in the Future of Privacy Forum’s […]
Nothing to Hide: Tools for Talking (and Listening) About Data Privacy for Integrated Data Systems
Data-driven and evidence-based social policy innovation can help governments serve communities better, smarter, and faster. Integrated Data Systems (IDS) use data that government agencies routinely collect in the normal course of delivering public services to shape local policy and practice. They can use data to evaluate the effectiveness of new initiatives or bridge gaps between public services and community providers.
Empirical Research in the Internet of Things, Mobile Privacy, and Digital Advertising
In the world of consumer privacy, including the Internet of Things (IoT), mobile data, and advertising technologies (“Ad Tech”), it can often be difficult to measure real-world impact and conceptualize individual harms and benefits. Fortunately, academic researchers are increasingly focusing on these issues, leading to impressive scholarship from institutions such as the Princeton Center for Information Technology Policy (CITP), Carnegie Mellon University School of Computer Science, UC Berkeley School of Information, and many others, including non-profits and think tanks.
Unfairness By Algorithm: Distilling the Harms of Automated Decision-Making
Analysis of personal data can be used to improve services, advance research, and combat discrimination. However, such analysis can also create valid concerns about differential treatment of individuals or harmful impacts on vulnerable communities. These concerns can be amplified when automated decision-making uses sensitive data (such as race, gender, or familial status), impacts protected classes, or affects individuals’ eligibility for housing, employment, or other core services. When seeking to identify harms, it is important to appreciate the context of interactions between individuals, companies, and governments—including the benefits provided by automated decision-making frameworks, and the fallibility of human decision-making.