Privacy Enhancing Technologies (PETs) are crucial for safeguarding individual privacy while enabling socially beneficial research, data sharing, and advancements in sectors that collect or use sensitive data. As AI systems increasingly rely on vast amounts of data, PETs like differential privacy, homomorphic encryption, federated learning, zero-knowledge proofs, and even model contract clauses can help ensure that personal data remains confidential and secure. This is vital not only for protecting individual rights but also for earning and maintaining public trust in technologies that use data about people.
Ethically implementing PETs is also essential. PETs must be designed and deployed to protect marginalized groups and avoid practices that may appear to be privacy-preserving, but actually exploit sensitive data or undermine privacy. In addition, clear policy guidelines on the deployment of PETs are necessary to help make sound business cases for their use in industry. If PETs are ethically integrated into research and business practices, and if global regulators have an opportunity to support their appropriate deployment, there is enormous potential for increasing technological innovation and societal benefits while upholding privacy and public trust.
FPF supports the development of PETs, convenes experts from around the world to deploy best practices, and provides in-depth analysis of emerging technologies and their policy implications. FPF also leads the National Science Foundation and Department of Energy-funded Research Coordination Network for the use of PETs to advance socially beneficial data sharing and protect people’s privacy as data is increasingly used to train AI models. FPF also facilitates a Global PETs Network of regulators who are interested in the lawful and ethical implementation of these tools.
NEW FPF REPORT: Confidential Computing and Privacy: Policy Implications of Trusted Execution Environments
Written by Judy Wang, FPF Communications Intern Today, the Future of Privacy Forum (FPF) published a paper on confidential computing, a privacy-enhancing technology (PET) that marks a significant shift in the trustworthiness and verifiability of data processing for the use cases it supports, including training and use of AI models. Confidential computing leverages two key […]