A team of computer scientists at UC Riverside has developed a method to erase private and copyrighted data from artificial intelligence models—without needing access to the original training data.
A team of computer scientists at UC Riverside has developed a method to erase private and copyrighted data from artificial intelligence models—without needing access to the original training data.
This advance, detailed in a paper presented in July at the International Conference on Machine Learning in Vancouver, Canada, addresses a rising global concern about personal and copyrighted materials remaining in AI models indefinitely—and thus accessible to model users—despite efforts by the original creators to delete or guard their information with paywalls and passwords.
The study was also published on the arXiv preprint server.
The UCR innovation compels AI models to „forget“ selected information while maintaining the models‘ functionality with the remaining data. It’s a significant advancement that can amend models without having to re-make them with the voluminous original training data, which is costly and energy-intensive. The approach also enables the removal of private information from AI models even when the original training data is no longer available.
„In real-world situations, you can’t always go back and get the original data“, said Ümit Yiğit Başaran, a UCR electrical and computer engineering doctoral student and lead author of the study. „We’ve created a certified framework that works even when that data is no longer available.
