Workshop
Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ East Meeting Rooms 8 + 15
Privacy in Machine Learning (PriML)
The goal of our workshop is to bring together privacy experts working in academia and industry to discuss the present and the future of privacy-aware technologies powered by machine learning. The workshop will focus on the technical aspects of privacy research and deployment with invited and contributed talks by distinguished researchers in the area. The programme of the workshop will emphasize the diversity of points of view on the problem of privacy. We will also ensure there is ample time for discussions that encourage networking between researches, which should result in mutually beneficial new long-term collaborations.
| Opening | |
| Privacy for Federated Learning, and Federated Learning for Privacy (Invited talk) | |
| Gaussian Differential Privacy (Contributed talk) | |
| QUOTIENT: Two-Party Secure Neural Network Training & Prediction (Contributed talk) | |
| Coffee break (Break) | |
| Fair Decision Making using Privacy-Protected Data (Invited talk) | |
| Spotlight talks | |
| Poster Session | |
| Lunch break (Break) | |
| Fair Universal Representations via Generative Models and Model Auditing Guarantees (Invited talk) | |
| Pan-Private Uniformity Testing (Contributed talk) | |
| Private Stochastic Convex Optimization: Optimal Rates in Linear Time (Contributed talk) | |
| Coffee break (Break) | |
| Formal Privacy At Scale: The 2020 Decennial Census TopDown Disclosure Limitation Algorithm (Invited talk) | |
| Panel Discussion (Discussion Panel) | |
| Closing | |