BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Sanjeev Arora (Princeton University and IAS) DTSTART:20200625T190000Z DTEND:20200625T203000Z DTSTAMP:20260423T003239Z UID:IASML/8 DESCRIPTION:Title: In stance-Hiding Schemes for Private Distributed Learning\nby Sanjeev Aro ra (Princeton University and IAS) as part of IAS Seminar Series on Theoret ical Machine Learning\n\n\nAbstract\nAn important problem today is how to allow multiple distributed entities to train a shared neural network on th eir private data while protecting data privacy. Federated learning is a st andard framework for distributed deep learning Federated Learning\, and on e would like to assure full privacy in that framework . The proposed metho ds\, such as homomorphic encryption and differential privacy\, come with d rawbacks such as large computational overhead or large drop in accuracy. T his work introduces a new and simple encryption of training data\, which h ides the information in it and allows its use in the usual deep learning p ipeline. The encryption is inspired by classic notion of instance-hiding i n cryptography. Experiments show that it allows training with fairly small effect on final accuracy.\n\nWe also give some theoretical analysis of pr ivacy guarantees for this encryption\, showing that violating privacy requ ires attackers to solve a difficult computational problem.\n\nJoint work w ith Yangsibo Huang\, Zhao Song\, and Kai Li. To appear at ICML 2020.\n LOCATION:https://researchseminars.org/talk/IASML/8/ END:VEVENT END:VCALENDAR