Rutgers Business School researchers win international privacy enhancing technologies challenge

A team of privacy researchers from the Rutgers Institute for Data Science, Learning, and Applications (I-DSLA) led by Distinguished Professor and Department Vice Chair Jaideep Vaidya (Management Science and Information Systems), his postdoctoral research associate Hafiz Asif, and two of his Ph.D. students, Sitao Min and Xinyue Wang, won first place in the “Financial Crime Prevention” track of the U.K.-U.S. joint sponsored Privacy Enhancing Technologies (PETS) Prize Challenge. 

The competition featured world-leading experts from top academic institutions, global technology companies, and privacy start-ups who competed for cash prizes from a combined U.S.-U.K. prize pool of $1.6 million. The winners were announced at the Summit for Democracy (3/30/2023) hosted by the U.S. Department of State.   

According to the official White House press release (, “the winning solutions combined different PETs to allow the artificial intelligence (AI) models to learn to make better predictions without exposing any sensitive data. In the final phase of the challenges, the privacy guarantees of the ‘blue teams’ solutions were put to the test by the ‘red teams’ attempts to reveal the original data used for training the models. The resilience of the solutions to these attacks determined the final winners.

“This focus on combining privacy approaches encouraged the development of innovative solutions that address practical data privacy concerns in real-world scenarios.”

Speaking for the team, Professor Vaidya said, “Machine learning applied over large datasets has the potential to result in significant scientific and societal advances. However, we strongly believe that the benefits of machine learning do not need to be at the cost of our individual liberty, including privacy and equity. There is an urgent need to devise methods that can enable the socially responsible use of machine learning technologies and demonstrate their use in practice. The PETs Challenge was the ideal opportunity to show that machine learning can be privacy enhancing and that practical systems can be realized that provide both privacy and utility.

Vaidya continued, “I am glad to say that this competition demonstrated the quality of our students and the diversity of intellectual thought at RBS and Rutgers, which is what distinguishes us from other top academic institutions.”

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