New FPF Report Highlights Privacy Tech Sector Evolving from Compliance Tools to Platforms for Risk Management and Data Utilization
As we enter the third phase of development of the privacy tech market, purchasers are demanding more integrated solutions, product offerings are more comprehensive, and startup valuations are higher than ever, according to a new report from the Future of Privacy Forum and Privacy Tech Alliance. These factors are leading to companies providing a wider […]
FPF Report Outlines Opportunities to Mitigate the Privacy Risks of AR & VR Technologies
A new report from the Future of Privacy Forum (FPF), Augmented Reality + Virtual Reality: Privacy & Autonomy Considerations in Emerging, Immersive Digital Worlds, provides recommendations to address the privacy risks of augmented reality (AR) and virtual reality (VR) technologies. The vast amount of sensitive personal information collected by AR and VR technologies creates serious risks […]
Policy Brief: Location Data Under Existing Privacy Laws
The Future of Privacy Forum released a new policy brief, Location Data Under Existing Privacy Laws. Defining and regulating location data in a privacy law can be an elusive challenge. In part, this is due to its ubiquity in our lives: information about how devices and people move through spaces over time is utilized by Wi-Fi […]
FPF and Privacy Analytics Identify “A Practical Path Toward Genetic Privacy”
Paper highlights de-identification standards, re-identification research, and emerging technical, contractual, and policy protections that can safeguard genetic data while supporting research. Genomic data is arguably the most personal of all personally identifiable information (“PII”). Techniques to de-identify genomic data to limit privacy and security risks to individuals–while that data is used for research and statistical […]
The Future of Privacy Technology
Today, FPF is making available a report co-authored by CEO Jules Polonetsky and Policy Fellow Jeremy Greenberg that identifies future directions and requirements of privacy technology from the industry perspective. With support by the National Science Foundation under Grant No. 1939288, a survey was designed and administered to industry privacy leaders to provide input on […]
Privacy 2020: 10 Privacy Risks and 10 Privacy Enhancing Technologies to Watch in the Next Decade
Today, FPF is publishing a white paper co-authored by CEO Jules Polonetsky and hackylawyER Founder Elizabeth Renieris to help corporate officers, nonprofit leaders, and policymakers better understand privacy risks that will grow in prominence during the 2020s, as well as rising technologies that will be used to help manage privacy through the decade. Leaders must understand […]
A Privacy Playbook for Connected Car Data
Drivers and passengers expect cars to be safe, comfortable, and trustworthy. Individuals often consider the details of their travels—and the vehicles that take them between their home, the office, a hospital, their place of worship, or their child’s school—to be sensitive, personal data. The newest cars contain numerous sensors, from cameras and GPS to accelerometers […]
New White Paper Provides Guidance on Embedding Data Protection Principles in Machine Learning
Immuta and the Future of Privacy Forum (FPF) today released a working white paper, Data Protection by Process: How to Operationalise Data Protection by Design for Machine Learning, that provides guidance on embedding data protection principles within the life cycle of a machine learning model. Data Protection by Design (DPbD) is a core data protection requirement […]
Comparing Privacy Laws: GDPR v. CCPA
In November 2018, OneTrust DataGuidance and FPF partnered to publish a guide to the key differences between the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act of 2018 (CCPA). Since then, a series of bills, signed by the California Governor on 11 October 2019, amended the CCPA to exempt from its application […]
Warning Signs: Identifying Privacy and Security Risks to Machine Learning Systems
FPF is working with Immuta and others to explain the steps machine learning creators can take to limit the risk that data could be compromised or a system manipulated.