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Workshop
Privacy Preserving Machine Learning - PriML and PPML Joint Edition
Borja Balle · James Bell · Aurélien Bellet · Kamalika Chaudhuri · Adria Gascon · Antti Honkela · Antti Koskela · Casey Meehan · Olga Ohrimenko · Mi Jung Park · Mariana Raykova · Mary Anne Smart · Yu-Xiang Wang · Adrian Weller

Fri Dec 11 01:20 AM -- 01:25 PM (PST) @
in Workshops Fri »
Event URL: https://ppml-workshop.github.io/ »

This one day workshop focuses on privacy preserving techniques for machine learning and disclosure in large scale data analysis, both in the distributed and centralized settings, and on scenarios that highlight the importance and need for these techniques (e.g., via privacy attacks). There is growing interest from the Machine Learning (ML) community in leveraging cryptographic techniques such as Multi-Party Computation (MPC) and Homomorphic Encryption (HE) for privacy preserving training and inference, as well as Differential Privacy (DP) for disclosure. Simultaneously, the systems security and cryptography community has proposed various secure frameworks for ML. We encourage both theory and application-oriented submissions exploring a range of approaches listed below. Additionally, given the tension between the adoption of machine learning technologies and ethical, technical and regulatory issues about privacy, as highlighted during the COVID-19 pandemic, we invite submissions for the special track on this topic.

Author Information

Borja Balle (DeepMind)
James Bell (Alan Turing Institute)
Aurélien Bellet (INRIA)
Kamalika Chaudhuri (UCSD)
Adria Gascon (Alan Turing Institute and Warwick university)
Antti Honkela (University of Helsinki)
Antti Koskela (University of Helsinki)
Casey Meehan (University of California, San Diego)
Olga Ohrimenko (The University of Melbourne)
Mi Jung Park (MPI-IS Tuebingen)
Mariana Raykova (Google)
Mary Anne Smart (University of California, San Diego)
Yu-Xiang Wang (UC Santa Barbara)
Adrian Weller (Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller MBE is a Director of Research in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. He is a Turing AI Fellow in Trustworthy Machine Learning, and heads Safe and Ethical AI at The Alan Turing Institute, the UK national institute for data science and AI. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards and previously held senior roles in finance.

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