Privacy Preserving Machine Learning

CCS 2019 Workshop
London, November 15


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

Submission deadline: June 21, 2019 (11:59pm AoE)
Notification of acceptance: August 7, 2019
CCS early registration deadline: TBD
Workshop: November 15, 2019

Submission Instructions

Submissions in the form of extended abstracts must be at most 4 pages long (not including references) single column 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.

Submit Your Abstract

Invited Speakers


Workshop organizers

  • Borja Balle
  • Adrià Gascón (Alan Turing Institute & Warwick)
  • Olya Ohrimenko (Microsoft Research)
  • Mariana Raykova (Google)
  • Phillipp Schoppmmann (HU Berlin)
  • Carmela Troncoso (EPFL)

Program Committee

  • Pauline Anthonysamy (Google)
  • Brendan Avent (USC)
  • Carsten Baum (BIU)
  • Aurélien Bellet (Inria)
  • Elette Boyle (IDC Herzliya)
  • Kamalika Chaudhuri (UCSD)
  • Giovanni Cherubin (EPFL)
  • Graham Cormode (University of Warwick)
  • Morten Dahl (Dropout Labs)
  • Christos Dimitrakakis (Chalmers University of Technology)
  • Jack Doerner (Northeastern)
  • Jamie Hayes (UCL)
  • Dali Kaafar (Macquarie University and Data61-CSIRO)
  • Peter Kairouz (Google)
  • Shiva Kasiviswanathan (Amazon)
  • Marcel Keller (Data61)
  • Niki Kilbertus (Cambridge University)
  • Ágnes Kiss (TU Darmstadt)
  • Nadin Kokciyan (King's College London)
  • Boris Köpf (Microsoft Research)
  • Aleksandra Korolova (USC)
  • Ashwin Machanavajjhala (Duke University)
  • Eleftheria Makri (KU Leuven)
  • Sebastian Meiser (Visa)
  • Luca Melis (Amazon)
  • Kartik Nayak (Duke University)
  • Catuscia Palamidessi (École Polytechnique & INRIA)
  • Peter Rindal (Visa Research)
  • Benjamin Rubinstein (University of Melbourne)
  • Anand Sarwate (Rutgers University)
  • Thomas Schneider (TU Darmstadt)
  • Peter Scholl (Aarhus University)
  • Or Sheffet (University of Alberta)
  • Nigel Smart (KU Leuven)
  • Adam Smith (Boston University)
  • Florian Tramer (Stanford)
  • Muthuramakrishnan Venkitasubramaniam (Rochester)
  • Xiao Wang (Northwestern University)
  • Kevin Yeo (Google)
  • Pinar Yolum (Utrecht University)
  • Yang Zhang (CISPA Helmholtz Center)

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