Privacy in Machine Learning and Artificial Intelligence

FAIM 2018 Workshop | Stockholm, July 14 | Stockholmmässan Stockholm



The one-day workshop focuses on the technical aspects of privacy research with invited and contributed talks by distinguished researchers in the area. We will conclude the workshop with a panel discussion about ethical and regulatory aspects. The programme of the workshop will emphasize the diversity of points of view on the problem of privacy, exemplified by the approaches pursued by specific sub-communities scattered across the different meetings comprising the Federated Artificial Intelligence Meeting. We will also ensure there is ample time for discussions that encourage networking between researches from these different sub-communities, which should result in mutually beneficial new long-term collaborations.

Invited Speakers

  • Úlfar Erlingsson (Google)
  • Pınar Yolum (Utrecht)


8.45 Welcome and Introduction
9.00 -
09.45 -
10.30 Coffee Break
11.00 -
12.15 Poster Spotlights
13.00 Lunch Break
15.00 Coffee Break
15.30 Poster Session
18.00 Wrap Up

Accepted Papers

Travel Grants

Grants are available to help partially cover the travel expenses of students and researchers attending the workshop. Each grant will reimburse registration costs and travel expenses up to a maximum of 700 euros. We might be unable to provide awards to all applicants, in which case awards will be determined by the organizers based on the application material.

Applications are due on June 4, 2018.

An application for a travel award will consist of a single PDF file with a justification of financial needs, a summary of research interests, and a brief discussion of why the applicant will benefit from participating in the workshop. Please send your applications to with the subject title "PiMLAI Travel Grant".

Sponsored by:

Call For Papers & Important Dates

Download Full CFP Submit Your Abstract

Abstract submission: May 14, 2018 (11pm59 CET)
Notification of acceptance: May 29, 2018
Workshop: July 14, 2018

We invite submissions of recent work on privacy in machine learning and artificial intelligence, both theory and application-oriented. Similarly to how ICML, IJCAI, AAMAS, and other FAIM workshops are organized, all accepted abstracts will be part of a poster session held during the workshop. Additionally, the PC will select a subset of the abstracts for short oral presentations. At least one author of each accepted abstract is expected to represent it at the workshop.

Submissions in the form of extended abstracts must be at most 2 pages long (not including references) and adhere to the ICML format. We do accept submissions of work recently published or currently under review. Submissions do not need to be anonymized. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have their work published on the workshop webpage.

Solicited topics include, but are not limited to:

  • Differential privacy: theory, applications, and implementations

  • Privacy in internet of things and multi-agent systems

  • Privacy-preserving machine learning

  • Trade-offs between privacy and utility

  • Programming languages for privacy-preserving data analysis

  • Statistical notions of privacy, including relaxations of differential 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

  • Normative approaches to privacy in AI

  • Privacy in autonomous systems

  • Online social networks privacy


Workshop organizers

  • Borja Balle (Amazon Research Cambridge)
  • Antti Honkela (University of Helsinki)
  • Kamalika Chaudhuri (UCSD CSE)
  • Beyza Ermis (Amazon Research Berlin)
  • Jose Such (King's College London)
  • Mijung Park (MPI Tuebingen)

Program Committee

  • Adria Gascon (Turing Institute)
  • Anand Sarwate (Rutgers University)
  • Aurelien Bellet (INRIA)
  • Carmela Troncoso (EPFL)
  • Christos Dimitrakakis (Chalmers University)
  • Emiliano De Cristofaro (UCL)
  • Gaurav Misra (University of New South Wales)
  • Joseph Geumlek (UCSD CSE)
  • Marco Gaboardi (University of Buffalo, SUNY)
  • Maziar Gomrokchi (McGill University)
  • Michael Brueckner (Amazon Research Berlin)
  • Nadin Kokciyan (King's College London)
  • Olya Ohrimenko (Microsoft Research)
  • Ozgur Kafali (University of Kent)
  • Pauline Anthonysamy (Google)
  • Peter Kairouz (Stanford University)
  • Phillipp Schoppmann (Humboldt)
  • Shuang Song (UCSD CSE)
  • Yu-Xiang Wang (Amazon AWS)

  • Sponsors