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Privacy in Machine Learning (PriML)
Borja Balle · Kamalika Chaudhuri · Antti Honkela · Antti Koskela · Casey Meehan · Mi Jung Park · Mary Anne Smart · Mary Anne Smart · Adrian Weller

Sat Dec 08:00 AM -- 06:00 PM PST @ East Meeting Rooms 8 + 15
Event URL: https://priml-workshop.github.io/priml2019/ »

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.

08:10 AM Opening
08:15 AM Privacy for Federated Learning, and Federated Learning for Privacy (Invited talk)|| Brendan McMahan
09:05 AM Gaussian Differential Privacy (Contributed talk)|| Jinshuo Dong, Aaron Roth
09:25 AM QUOTIENT: Two-Party Secure Neural Network Training & Prediction (Contributed talk)|| Nitin Agrawal, Matt Kusner, Adria Gascon
09:45 AM Coffee break (Break)||
10:30 AM Fair Decision Making using Privacy-Protected Data (Invited talk)|| Ashwin Machanavajjhala
11:20 AM Spotlight talks
11:30 AM Poster Session
ccanonne Canonne, Kwang-Sung Jun, Seth Neel, Di Wang, giuseppe vietri, Liwei Song, Jonathan Lebensold, Huanyu Zhang, Lovedeep Gondara, Ang Li, FatemehSadat Mireshghallah, Jinshuo Dong, Anand D Sarwate, Antti Koskela, Joonas Jälkö, Matt Kusner, Dingfan Chen, Mi Jung Park, Ashwin Machanavajjhala, Jayashree Kalpathy-Cramer, , Vitaly Feldman, Andrew Tomkins, Hai Phan, Hossein Esfandiari, Mimansa Jaiswal, Mrinank Sharma, Jeff Druce, Casey Meehan, Zhengli Zhao, Hsiang Hsu, Davis Railsback, Abraham Flaxman, , Julius Adebayo, Aleksandra Korolova, Jiaming Xu, Naoise Holohan, Samyadeep Basu, Matthew Joseph, My Thai, Dana Yang, Ellen Vitercik, Michael Hutchinson, Chenghong Wang, Gregory Yauney, Yuchao Tao, Chao Jin, Sky Lee, Audra McMillan, Rauf Izmailov, Jiayi Guo, Siddharth Swaroop, Tribhuvanesh Orekondy, Hadi Esmaeilzadeh, Kevin Procopio, Alkis Polyzotis, Jafar Mohammadi, Nitin Agrawal
12:30 PM Lunch break (Break)||
02:00 PM Fair Universal Representations via Generative Models and Model Auditing Guarantees (Invited talk)|| Lalitha Sankar
02:50 PM Pan-Private Uniformity Testing (Contributed talk)|| Kareem Amin, Matthew Joseph
03:10 PM Private Stochastic Convex Optimization: Optimal Rates in Linear Time (Contributed talk)|| Vitaly Feldman, Tomer Koren, Kunal Talwar
03:30 PM Coffee break (Break)||
04:15 PM Formal Privacy At Scale: The 2020 Decennial Census TopDown Disclosure Limitation Algorithm (Invited talk)|| Philip Leclerc
05:05 PM Panel Discussion (Discussion Panel)||
05:55 PM Closing

Author Information

Borja Balle (Amazon)
Kamalika Chaudhuri (UCSD)
Antti Honkela (University of Helsinki)
Antti Koskela (University of Helsinki)
Casey Meehan (University of California, San Diego)
Mi Jung Park (MPI-IS Tuebingen)
Mary Anne Smart (UC San Diego)
Mary Anne Smart (University of California, San Diego)
Adrian Weller (Cambridge, Alan Turing Institute)

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Senior Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he leads the project on Trust and Transparency. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

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