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We are pleased to see Facebook’s response, but are looking forward to understanding how to best address the broader issues for all stakeholders.  These issues are important to discuss – they are not going away.

This Report first describes inherent privacy risks in an open data landscape, with an emphasis on potential harms related to re-identification, data quality, and fairness. To address these risks, the Report includes a Model Open Data Benefit-Risk Analysis (“Model Analysis”). The Model Analysis evaluates the types of data contained in a proposed open dataset, the potential benefits – and concomitant risks – of releasing the dataset publicly, and strategies for effective de-identification and risk mitigation.

Today, the Future of Privacy Forum released its City of Seattle Open Data Risk Assessment. The Assessment provides tools and guidance to the City of Seattle and other municipalities navigating the complex policy, operational, technical, organizational, and ethical standards that support privacy-protective open data programs.

The transparency goals of the open data movement serve important social, economic, and democratic functions in cities like Seattle. At the same time, some municipal datasets about the city and its citizens’ activities carry inherent risks to individual privacy when shared publicly. In 2016, the City of Seattle declared in its Open Data Policy that the city’s data would be “open by preference,” except when doing so may affect individual privacy.[1] To ensure its Open Data Program effectively protects individuals, Seattle committed to performing an annual risk assessment and tasked the Future of Privacy Forum (FPF) with creating and deploying an initial privacy risk assessment methodology for open data.

Notes from FPF On December 12, 2017, FPF announced the winners of our 8th Annual Privacy Papers for Policymakers (PPPM) Award. This Award recognizes leading privacy scholarship that is relevant to policymakers in the United States Congress, at U.S. federal agencies, and for data protection authorities abroad. In this special issue of the Scholarship Reporter, you […]

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.

The Future of Privacy Forum tracks student privacy news very closely, and shares relevant news stories with our newsletter subscribers. Approximately every month, we post “The Top 10,” a blog with our top student privacy stories.

Washington, DC – Today, the Future of Privacy Forum released a new study, Understanding Corporate Data Sharing Decisions: Practices, Challenges, and Opportunities for Sharing Corporate Data with Researchers. In this report, FPF reveals findings from research and interviews with experts in the academic and industry communities. Three main areas are discussed: 1) The extent to which leading companies make data available to support published research that contributes to public knowledge; 2) Why and how companies share data for academic research; and 3) The risks companies perceive to be associated with such sharing, as well as their strategies for mitigating those risks.