This one-day workshop focuses on privacy-preserving machine learning techniques for large-scale data analysis, both in the distributed and centralized settings, and on scenarios that highlight the importance and need for these techniques (e.g., via privacy attacks). There is growing interest from the Machine Learning (ML) community in leveraging cryptographic techniques such as Multi-Party Computation (MPC) and Homomorphic Encryption (HE) for secure computation during training and inference, as well as Differential Privacy (DP) for limiting the privacy risks from the trained model itself. We encourage both theory and application-oriented submissions exploring a range of approaches listed below.
Submission deadline: September
16 17, 2021 (UTC)
Notification of acceptance: October 15, 2021
Video and slides submission deadline (for accepted papers): November 1, 2021
Event date: December 14, 2021
Contact : firstname.lastname@example.org
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 encourage the submission of work that is new to the privacy-preserving machine learning community. Submissions solely based on work that has been previously published in conferences on machine learning and related fields are not suitable for the workshop. On the other hand, we allow submission of works currently under submission and relevant works recently previously published in privacy and security venues. Submission of work under review at NeurIPS 2021 is allowed but this must be disclosed at submission time. Submissions accepted to the NeurIPS main conference may be deprioritized in selecting oral presentations. The workshop will not have formal proceedings, but authors of accepted abstracts can choose to have a link to arxiv or a pdf added on the workshop webpage.