Private ML @ ICLR 2024

Recent advances in artificial intelligence greatly benefit from data-driven machine learning methods that train deep neural networks with large scale data. The usage of data should be responsible, transparent, and comply with privacy regulations. This workshop aims to bring together industry and academic researchers, privacy regulators and legal, policy people to have a conversation on privacy research. We hope to (re)visit major privacy considerations from both technical and nontechnical perspectives through interdisciplinary discussions. Topics of interest include, but are not limited to, the following:

  • Relationship of privacy regulation (such as GDPR, DMA) to machine learning
  • Interpolation and explanation of data privacy
  • Efficient methods for privacy preserving machine learning
  • Federated learning for data minimization
  • Differential privacy theory and practice
  • Threat model and privacy attacks
  • Encryption methods for machine learning
  • Privacy in machine learning systems
  • Privacy for large language models
  • Relationship between privacy, transparency, auditability, verifiability
  • Relationship between privacy, robustness, fairness etc


Call for Papers

This workshop is accepted to ICLR as virtual. We will update with more information later, but please make your travel plan accordingly.

Important Dates
  • Submission Due Date: February 4th February 9th, 2024, AoE
  • Notification of Acceptance: March 3rd, 2024, AoE
  • Camera-ready Papers Due: TBD
  • Workshop Dates: Saturday, May 11th, 2024, Vienna
Submission Instructions

Submissions should be double-blind, no more than 6 pages long (excluding references), and following the ICLR 2024 template. An optional appendix of any length can be put at the end of the draft (after references).

Submissions are processed in OpenReview. https://openreview.net/group?id=ICLR.cc/2024/Workshop/PML.

Our workshop does not have formal proceedings, i.e., it is non-archival. Accepted papers and their review comments will be posted on OpenReview in public (after the end of the review process), while rejected and withdrawn papers and their reviews will remain private.

We welcome submissions from novel research, ongoing (incomplete) projects, drafts currently under review at other venues, as well as recently published results. However, we request significant updates if the work has previously been presented at major machine learning conferences or workshops before May 1st, 2024.

Camera Ready Instructions

Coming soon.

Presentation Instructions

Coming soon.




Workshop Program

The following program is local time.

Local Time Activity
09:00AM - 09:05AM Introduction and Opening Remarks
09:05AM - 09:40AM Invited Talk 1
09:40AM - 10:00AM Spotlight Talks
10:00AM - 10:15AM Break
10:15AM - 10:50AM Invited Talk 2
10:50AM - 11:25AM Invited Talk 3
11:25AM - 12:30PM Poster
12:30PM - 13:30PM Lunch Break
13:30PM - 14:25PM Panel Discussions
14:25PM - 15:00PM Invited Talk 4
15:00PM - 15:15PM Break
15:15PM - 15:50PM Invited Talk 5
15:50PM - 16:20PM Spotlight Talks
16:20PM - 16:35PM Break
16:35PM - 17:10PM Invited Talk 6
17:10PM - 17:45PM Invited Talk 7
17:45PM - 17:50PM Concluding Remarks

Accepted Papers

Accepted Papers


Invited Speakers

Rachel Cummings

Columbia University

Dan Kifer

Penn State University

Kobbi Nissim

Georgetown University

Daniel Ramage


Janel Thamkul


Panel Discussion



Workshop Organizers

Salman Avestimehr

University of Southern California / FedML

Tian Li

University of Chicago / Meta

Niloofar (Fatemeh) Mireshghallah

University of Washington

Sewoong Oh

University of Washington / Google

Florian Tramer

ETH Zurich

Zheng Xu



Program Committee