How to ensure anonymity of AI systems? | Faculty of Science | University of Helsinki When training artificial intelligence systems, developers need to use privacy-enhancing technologies to ensure that the subjects of the training data are not exposed, new study suggests.

ELSA Board member @ahonkela.bsky.social contributed to the paper"Impact of Dataset Properties on Membership Inference Vulnerability of Deep Transfer Learning",presented at @neuripsconf.bsky.social'25

Article ➡️ www.helsinki.fi/en/faculty-s...

#MachineLearning #DifferentialPrivacy #PrivacyGuarantees