Privacy Preserving Machine Learning

NIPS 2018 Workshop
Montréal, December 8

Palais des Congrès de Montréal
Room TBA

Submissions open


This one day workshop focuses on privacy preserving techniques for training, inference, and disclosure in large scale data analysis, both in the distributed and centralized settings. We have observed increasing interest of the 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, including:

  • secure multi-party computation techniques for ML
  • homomorphic encryption techniques for ML
  • hardware-based approaches to privacy preserving ML
  • centralized and decentralized protocols for learning on encrypted data
  • differential privacy: theory, applications, and implementations
  • statistical notions of privacy including relaxations of differential privacy
  • empirical and theoretical comparisons between different notions of privacy
  • trade-offs between privacy and utility

We think it will be very valuable to have a forum to unify different perspectives and start a discussion about the relative merits of each approach. The workshop will also serve as a venue for networking people from different communities interested in this problem, and hopefully foster fruitful long-term collaboration.

Call For Papers & Important Dates

Download Full CFP Submit your paper

Submission deadline: October 8, 2018 (11:59pm AoE)
Notification of acceptance: November 1, 2018
Workshop: December 8, 2018

Submission Instructions

Submissions in the form of extended abstracts must be at most 4 pages long (not including references) and adhere to the NIPS format. We do accept submissions of work recently published or currently under review. Submissions should be anonymized. 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.

We can offer the opportunity to purchase a NIPS registration to one author of each accepted paper. Currently, we only have the information that is available at the Workshop FAQ and will provide updates as soon as we know more.

Invited Speakers

  • Kamalika Chaudhuri (University of California, San Diego )
  • Shafi Goldwasser (MIT & Weizmann Institute of Science)
  • Ian Goodfellow (Google Brain)
  • Adam Smith (Boston University)




Workshop organizers

  • Aurélien Bellet (Inria)
  • Adrià Gascón (Alan Turing Institute & Warwick)
  • Niki Kilbertus (MPI for Intelligent Systems & Cambridge)
  • Olya Ohrimenko (Microsoft Research)
  • Mariana Raykova (Yale)
  • Adrian Weller (Alan Turing Institute & Cambridge)

Program Committee

  • Pauline Anthonysamy (Google)
  • Borja de Balle Pigem (Amazon)
  • Battista Biggio (University of Cagliari)
  • Keith Bonawitz (Google)
  • Emiliano de Cristofaro (University College London)
  • David Evans (University of Virginia)
  • Joseph Geumlek (UCSD)
  • Irene Giacomelli (Wisconsin University)
  • Hamish Ivey-Law (Data61 & CSIRO)
  • Nadin Kokciyan (King's College London)
  • Aleksandra Korolova (USC)
  • Kim Laine (Microsoft Research)
  • Payman Mohassel (Visa Research)
  • Catuscia Palamidessi (École Polytechnique & INRIA)
  • Mijung Park (Max Planck Institute for Intelligent Systems)
  • Benjamin Rubinstein (University of Melbourne)
  • Anand Sarwate (Rutgers University)
  • Philipp Schoppmann (HU Berlin)
  • Nigel Smart (KU Leuven)
  • Carmela Troncoso (EPFL)
  • Pinar Yolum (Utrecht University)
  • Samee Zahur (Google)


Travel Grants

A limited number of travel grants will be available to help partially cover the expenses of authors of accepted papers. We might not be able to provide awards to all applicants, in which case awards will be determined by the organizers based on the application material.

Application details follow shortly.