Privacy in Machine Learning

NeurIPS 2019 Workshop
Vancouver, December ?

Scope

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. We encourage submissions exploring a broad range of research areas related to data privacy, including but not limited to:

  • Differential privacy: theory, applications, and implementations
  • Privacy-preserving machine learning
  • Trade-offs between privacy and utility
  • Programming languages for privacy-preserving data analysis
  • Statistical and information-theoretic notions of privacy
  • Empirical and theoretical comparisons between different notions of privacy
  • Privacy attacks
  • Policy-making aspects of data privacy
  • Secure multi-party computation techniques for machine learning
  • Learning on encrypted data, homomorphic encryption
  • Distributed privacy-preserving algorithms
  • Privacy in autonomous systems
  • Online social networks privacy
  • Interplay between privacy and adversarial robustness in machine learning
  • Relations between privacy, fairness and transparency

Call For Papers & Important Dates

Download Full CFP


Submission deadline: September 9, 2019, 23:59 UTC
Notification of acceptance: October 1, 2019
NeurIPS early registration deadline: October 23, 2019
Workshop: TBD

Submission Instructions

Submissions in the form of extended abstracts must be at most 4 pages long (not including references; additional supplementary material may be submitted but may be ignored by reviewers), non-anonymized and adhere to the NeurIPS format. We do accept submissions of work recently published or currently under review. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have a link to arxiv or a pdf published on the workshop webpage.

Submit Your Abstract

Invited Speakers

  • Philip Leclerc (US Census)
  • Ashwin Machanavajjhala (Duke University)
  • Brendan McMahan (Google)
  • Lalitha Sankar (Arizona State University)

Organization


Workshop organizers

  • Borja Balle (Amazon)
  • Kamalika Chaudhuri (UCSD)
  • Antti Honkela (University of Helsinki)
  • Antti Koskela (University of Helsinki)
  • Casey Meehan (UCSD)
  • Mijung Park (Max Planck Institute for Intelligent Systems)
  • Mary Anne Smart (UCSD)
  • Adrian Weller (Alan Turing Institute & Cambridge)

Program Committee

  • Gautam Kamath (University of Waterloo)
  • Kana Shimizu (Computational Biology Research Center, AIST)
  • Olga Ohrimenko (Microsoft)
  • Aurélien Bellet (INRIA)
  • Carmela Troncoso (Ecole Polytechnique Fédérale de Lausanne)
  • Nadin Kokciyan (King's College London)
  • Audra McMillan (Boston University and Northeastern University)
  • Adria Gascon (The Alan Turing Institute / Warwick University)
  • Matt Fredrikson (Carnegie Mellon University)
  • Yu-Xiang Wang (Carnegie Mellon University)
  • Aleksandra Korolova (University of Southern California)
  • Mark Bun (Boston University)
  • James Bell (University of Cambridge)
  • Alon Gonen (Princeton University)
  • Marcel Keller (Data61)
  • James Foulds (University of California San Diego)
  • Jun Sakuma (University of Tsukuba)
  • Or Sheffet (University of Alberta)
  • Christos Dimitrakakis (Chalmers University / University of Lille / Harvard University)
  • Anand Sarwate (Rutgers University)
  • Kunal Talwar (Google)
  • Peter Kairouz (Google AI)
  • To Be Completed...