“Building and scaling privacy-enhancing technologies is a key investment for us, not only to improve our own products but also to open-source innovations for use across the industry. We’re excited to champion these innovative scholars and look forward to their long-term impact on enhancing privacy.”
—Mike Clark, Director of Privacy Product Management

In March, Meta launched the 2022 Privacy-Enhancing Technologies (PET) request for proposals (RFP). Today, we’re announcing the winners of this award.

By integrating privacy-enhancing technologies into our products, we are building trustworthy experiences that billions of people use worldwide. Our primary goal with this program is to help design and deploy new privacy-enhancing solutions that minimize and secure the data we collect, process, and share, while providing products people love to use and supporting businesses across Meta Technologies. As we continue making strides in privacy-enhancing technologies at Meta, one of the key elements in our privacy program is learning from outside experts.

Following on the success of the 2020 and 2021 Privacy-Enhancing Technologies RFPs, in which recipient research was accepted in notable conferences, including NeurIPS and ICML, the 2022 PETs RFP continues our tradition of supporting privacy-focused projects in academia with potentially broad application benefiting a wide range of industries and constituencies. This year, areas of interest included the following:

• Privacy-preserving analytics
• Private record linkage and aggregation
• Privacy-preserving machine learning
• Privacy of messaging
• Anonymous credentials
• Privacy-preserving techniques in Data for Good
• Privacy in Metaverse

The 2022 PETs RFP attracted 161 proposals from 108 universities and institutions around the world. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.

Research award recipients

Beyond FL: Truly decentralized ML with privacy and robustness
Nicolas Papernot (University of Toronto), Somesh Jha (University of Wisconsin–Madison), Xiao Wang (Northwestern University)

Data auditing for machine learning models
Kai Li (Princeton University), Sanjeev Arora, Xiaoxiao Li (University of British Columbia)

Differential privacy for multi-relational databases
Xi He (University of Waterloo)

Efficient algorithms for federated privacy preserving ML
Sebastian Urban Stich (CISPA Helmholtz Center for Information Security)

Fast, robust, and scalable privacy preserving data analytics
Peihan Miao (Brown University), Mohammad Hajiabadi (University of Waterloo)

Improved redaction technologies for data sharing
Blase Ur, Emma Peterson (University of Chicago)

Interoperable encrypted messaging
Paul Grubbs (University of Michigan)

Mitigating threat of re-identification from eye tracking data
Eakta Jain, Kevin Butler (University of Florida)

Private multi-task learning
Virginia Smith, Steven Wu (Carnegie Mellon University)

Protecting location privacy for augmented reality
Yidan Hu (Rochester Institute of Technology), Zhengxiong Li (University of Colorado Denver)