Workshop

Privacy in Machine Learning (PriML) 2021

Yu-Xiang Wang · Borja Balle · Giovanni Cherubin · Kamalika Chaudhuri · Antti Honkela · Jonathan Lebensold · Casey Meehan · Mi Jung Park · Adrian Weller · Yuqing Zhu

Abstract Workshop Website
Tue 14 Dec, 1:20 a.m. PST

Abstract:

The goal of our workshop is to bring together privacy experts working in academia and industry to discuss the present and future of technologies that enable machine learning with privacy. The workshop will focus on the technical aspects of privacy research and deployment with invited and contributed talks by distinguished researchers in the area. By design, the workshop should serve as a meeting point for regular NeurIPS attendees interested/working on privacy to meet other parts of the privacy community (security researchers, legal scholars, industry practitioners). The focus this year will include emerging problems such as machine unlearning, privacy-fairness tradeoffs and legal challenges in recent deployments of differential privacy (e.g. that of the US Census Bureau). We will conclude the workshop with a panel discussion titled: “Machine Learning and Privacy in Practice: Challenges, Pitfalls and Opportunities”. A diverse set of panelists will address the challenges faced applying these technologies to the real world. The programme of the workshop will emphasize the diversity of points of view on the problem of privacy. We will also ensure that there is ample time for discussions that encourage networking between researchers, which should result in mutually beneficial new long-term collaborations.

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Timezone: America/Los_Angeles »

Schedule