iiinova — where your digital self keeps evolving

Reposted by Nicolas Papernot

University of Toronto @utoronto.ca · 10/09/2026

For #UofT researcher Nicolas Papernot, the secret to strengthening cybersecurity in the age of AI is transparency – sharing information about threats & allowing researchers to probe these systems before attackers do. 💻 uoft.me/cE4

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Reposted by Nicolas Papernot

Schwartz Reisman Institute for Technology and Society @torontosri.bsky.social · 08/09/2026

What do new AI capabilities mean for Canada's critical digital infrastructure, economy, & national security? Join @nicolaspapernot.bsky.social, @alexposadzki.bsky.social, Avi Goldfarb (@utoronto.ca), Kathryn Hume (@vectorinstitute.ai ), & Samir Chhabra (ISED) to find out! Register: shorturl.at/RSOhK

Join Samir Chhabra (Innovation, Science and Economic Development Canada), Kathryn Hume (Vector Institute), Alexandra Posadzki (The Globe and Mail), and University of Toronto's Avi Goldfarb and Nicolas Papernot in-person on 10 September for a discussion on AI-powered threats and Canada's future.

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 09/09/2026

The submission server for #SaTML2027 is now open! There are also some changes to the Call for Papers, diffs are indicated on the website. Dates: Mandatory abstract deadline: Sep 22 Paper deadline: Sep 29 satml.org

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Reposted by Nicolas Papernot

Schwartz Reisman Institute for Technology and Society @torontosri.bsky.social · 03/09/2026

How is AI changing the cybersecurity threat landscape? Join @nicolaspapernot.bsky.social and @alexposadzki.bsky.social in-person on Sept. 10 to find out! 📅 Sept. 10, 2026 🕓 4:00 PM – 5:30 PM 📍 Schwartz Reisman Innovation Campus Limited seats available! Register: nicolaspapernot.rsvpify.com

Nicolas Papernot, associate professor in the University of Toronto’s Department Electrical and Computer Engineering and an expert in AI security.

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 23/07/2026

On top of our Call for Papers, #SaTML2027 has a Call for Competitions and a Call for Workshops (first time ever!!). Dates (easy to remember, same for both) Deadline: August 28, 2026 Notifications: September 18, 2026 Topics can be anything in trustworthy and secure ML!

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 29/06/2026

We're pleased to announce that #SaTML2027 will be in Reykjavik, Iceland! Thanks to General Chair Giovanni Apruzzese for making the arrangements. The conference will be in early May 2027. Get your submissions ready: the deadline will be September 29, 2026. Details on the new website: satml.org

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Nicolas Papernot @nicolaspapernot.bsky.social · 03/06/2026

We discovered that it is possible to create an AI-driven computer worm using an open-weight AI model that anyone can download. This research was conducted in a lab walled off from the outside world, and shared only after removing details that could aid bad actors. www.nytimes.com/2026/06/02/t...

nytimes.com

Scientists Find Way to Supercharge Dangerous Computer ‘Worms’ With A.I.

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 06/04/2026

SaTML is looking for a host for #SaTML2027! If you're interested in bringing SaTML to a city near you, please fill out this form by April 15! 🏘️🏙️🏡🌆 docs.google.com/forms/d/e/1F...

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Nicolas Papernot @nicolaspapernot.bsky.social · 22/09/2025

Thank you to Samsung for the AI Researcher of 2025 award! I'm privileged to collaborate with many talented students & postdoctoral fellows @utoronto.ca @vectorinstitute.ai . This would not have been possible without them! It was a great honour to receive the award from @yoshuabengio.bsky.social !

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Reposted by Nicolas Papernot

Konrad Rieck 🌈 @rieck.mlsec.org · 03/09/2025

Three weeks to go until the SaTML 2026 deadline! ⏰ We look forward to your work on security, privacy, and fairness in AI. 🗓️ Deadline: Sept 24, 2025 We have also updated our Call for Papers with a statement on LLM usage, check it out: 👉 satml.org/call-for-pap... @satml.org

IEEE Conference on Secure and Trustworthy Machine Learning
Technical University of Munich, Germany
March 23–25, 2026

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Nicolas Papernot @nicolaspapernot.bsky.social · 23/07/2025

Thank you to @schmidtsciences.bsky.social for funding our lab's work on cryptographic approaches for verifiable guarantees in ML systems and for connecting us to other groups working on these questions!

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Reposted by Nicolas Papernot

Stephan Rabanser @stvrb.bsky.social · 11/07/2025

📄 Selective Prediction Via Training Dynamics Paper ➡️ arxiv.org/abs/2205.13532 Workshop ➡️ 3rd Workshop on High-dimensional Learning Dynamics (HiLD) Poster ➡️ West Meeting Room 118-120 on Sat 19 Jul 10:15 a.m. — 11:15 a.m. & 4:45 p.m. — 5:30 p.m.

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Reposted by Nicolas Papernot

Stephan Rabanser @stvrb.bsky.social · 11/07/2025

📄 Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings (✨ oral paper ✨) Paper ➡️ arxiv.org/abs/2505.22356 Poster ➡️ E-504 on Thu 17 Jul 4:30 p.m. — 7 p.m. Oral Presentation ➡️ West Ballroom C on Thu 17 Jul 4:15 p.m. — 4:30 p.m.

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Reposted by Nicolas Papernot

Stephan Rabanser @stvrb.bsky.social · 11/07/2025

📄 Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention TL;DR ➡️ We show that a model owner can artificially introduce uncertainty and provide a detection mechanism. Paper ➡️ arxiv.org/abs/2505.23968 Poster ➡️ E-1002 on Wed 16 Jul 11 a.m. — 1:30 p.m.

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Reposted by Nicolas Papernot

ePrint Updates @eprint.ing.bot · 02/06/2025

Secure Noise Sampling for Differentially Private Collaborative Learning (Olive Franzese, Congyu Fang, Radhika Garg, Somesh Jha, Nicolas Papernot, Xiao Wang, Adam Dziedzic) ia.cr/2025/1025

Abstract. Differentially private stochastic gradient descent (DP-SGD) trains machine learning (ML) models with formal privacy guarantees for the training set by adding random noise to gradient updates. In collaborative learning (CL), where multiple parties jointly train a model, noise addition occurs either (i) before or (ii) during secure gradient aggregation. The first option is deployed in distributed DP methods, which require greater amounts of total noise to achieve security, resulting in degraded model utility. The second approach preserves model utility but requires a secure multiparty computation (MPC) protocol. Existing methods for MPC noise generation require tens to hundreds of seconds of runtime per noise sample because of the number of parties involved. This makes them impractical for collaborative learning, which often requires thousands or more samples of noise in each training step.

We present a novel protocol for MPC noise sampling tailored to the collaborative learning setting. It works by constructing an approximation of the distribution of interest which can be efficiently sampled by a series of table lookups. Our method achieves significant runtime improvements and requires much less communication compared to previous work, especially at higher numbers of parties. It is also highly flexible – while previous MPC sampling methods tend to be optimized for specific distributions, we prove that our method can generically sample noise from statistically close approximations of arbitrary discrete distributions. This makes it compatible with a wide variety of DP mechanisms. Our experiments demonstrate the efficiency and utility of our method applied to a discrete Gaussian mechanism for differentially private collaborative learning. For 16 parties, we achieve a runtime of 0.06 seconds and 11.59 MB total communication per sample, a 230× runtime improvement and 3× less communication compared to the prior state-of-the-art for sampling from discrete Gaussian distribution in MPC.

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Nicolas Papernot @nicolaspapernot.bsky.social · 05/06/2025

Excited to share the first batch of research projects funded through the Canadian AI Safety Institute's research program at CIFAR! The projects will tackle topics ranging from misinformation to safety in AI applications to scientific discovery. Learn more: cifar.ca/cifarnews/20...

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Reposted by Nicolas Papernot

Stephan Rabanser @stvrb.bsky.social · 02/06/2025

📢 New ICML 2025 paper! Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention 🤔 Think model uncertainty can be trusted? We show that it can be misused—and how to stop it! Meet Mirage (our attack💥) & Confidential Guardian (our defense🛡️). 🧵1/10

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Nicolas Papernot @nicolaspapernot.bsky.social · 27/05/2025

If you are submitting to @ieeessp.bsky.social this year, a friendly reminder that there is an abstract submission deadline this Thursday May 29 (AoE). More details: sp2026.ieee-security.org/cfpapers.html

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Reposted by Nicolas Papernot

Asia Biega @asiabiega.bsky.social · 23/05/2025

As part of the theme Societal Aspects of Securing the Digital Society, I will be hiring PhD students and postdocs at #MPI-SP, focusing in particular on the computational and sociotechnical aspects of technology regulations and the governance of emerging tech. Get in touch if interested.

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 12/05/2025

🌍 Help shape the future of SaTML! We are on the hunt for a 2026 host city - and you could lead the way. Submit a bid to become General Chair of the conference: forms.gle/vozsaXjCoPzc...

forms.gle

Bid to host SaTML 2026

Thank you for considering to host SaTML! SaTML has been organized as a 3 day conference so far. We are looking for volunteers interested in finding a venue to host the conference in 2026. By submitti...

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Reposted by Nicolas Papernot

Tudor Cebere @tcebere.bsky.social · 21/04/2025

Excited to be in Singapore for ICLR, presenting our work on privacy auditing (w/ Aurélien & @nicolaspapernot.bsky.social). If you are interested in differential privacy/privacy auditing/security for ML, drop by (#497 26 Apr 10-12:30 pm) or let's grab a coffee! ☕ openreview.net/forum?id=xzK...

openreview.net

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model

Machine learning models can be trained with formal privacy guarantees via differentially private optimizers such as DP-SGD. In this work, we focus on a threat model where the adversary has access...

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 09/04/2025

👋 Welcome to #SaTML25! Kicking things off with opening remarks --- excited for a packed schedule of keynotes, talks and competitions on secure and trustworthy machine learning.

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Reposted by Nicolas Papernot

University of Toronto @utoronto.ca · 01/04/2025

Karina Vold says the rapid development of AI systems has left both philosophers & computer scientists grappling with difficult questions. #UofT 💻 uoft.me/bsp

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Nicolas Papernot @nicolaspapernot.bsky.social · 13/03/2025

Congratulations again, Stephan, on this brilliant next step! Looking forward to what you will accomplish with @randomwalker.bsky.social & @msalganik.bsky.social!

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Nicolas Papernot @nicolaspapernot.bsky.social · 12/03/2025

The Canadian AI Safety Institute (CAISI) Research Program at CIFAR is now accepting Expressions of Interest for Solution Networks in AI Safety under two themes: * Mitigating the Safety Risks of Synthetic Content * AI Safety in the Global South. cifar.ca/ai/ai-and-so...

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Nicolas Papernot @nicolaspapernot.bsky.social · 05/03/2025

I will be giving a talk at the MPI-IS @maxplanckcampus.bsky.social in Tübingen next week (March 12 @ 11am). The talk will cover my group's overall approach to trust in ML, with a focus on our work on unlearning and how to obtain verifiable guarantees of trust. Details: is.mpg.de/events/speci...

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Nicolas Papernot @nicolaspapernot.bsky.social · 31/01/2025

For Canadian colleagues, CIFAR and the CPI at UWaterloo are sponsoring a special issue "Artificial Intelligence Safety and Public Policy in Canada" in Canadian Public Policy / Analyse de politiques More details: www.cpp-adp.ca

cpp-adp.ca

English Menu

The December issue is available. o

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Nicolas Papernot @nicolaspapernot.bsky.social · 31/01/2025

One of the first components of the CAISI (Canadian AI Safety Institute) research program has just launched: a call for Catalyst Grant Projects on AI Safety. Funding: up to 100K for one year Deadline to apply: February 27, 2025 (11:59, AoE) More details: cifar.ca/ai/cifar-ai-...

cifar.ca

CIFAR AI Catalyst Grants - CIFAR

Encouraging new collaborations and original research projects in the field of machine learning, as well as its application to different sectors of science and society.

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Reposted by Nicolas Papernot

Konrad Rieck 🌈 @rieck.mlsec.org · 21/01/2025

The list of accepted papers for @satml.org 2025 is now online: 📃 satml.org/accepted-pap... If you’re intrigued by secure and trustworthy machine learning, join us April 9-11 in Copenhagen, Denmark 🇩🇰. Find more details here: 👉 satml.org/attend/

satml.org

Accepted Papers

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Nicolas Papernot @nicolaspapernot.bsky.social · 14/01/2025

If you work at the intersection of security, privacy, and machine learning, or more broadly how to trust ML, SaTML is a small-scale conference with highly-relevant work where you'll be able to have high-quality conversations with colleagues working in your area.

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Reposted by Nicolas Papernot

Conference on Secure and Trustworthy Machine Learning @satml.org · 20/12/2024

Hello world! The SaTML conference is now flying the blue skies! SaTML is the IEEE Conference on Secure and Trustworthy Machine Learning. The 2025 iteration, chaired by @someshjha.bsky.social @mlsec.org, will be in beautiful Copenhagen! Follow for the latest updates on the conference! satml.org

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Nicolas Papernot @nicolaspapernot.bsky.social · 12/12/2024

I look forward to co-directing the Canadian AI Safety Institute (CAISI) Research Program at CIFAR with @catherineregis.bsky.social We will be designing the program in the coming months and will soon share ways to get involved with this new community. Read more here: cifar.ca/cifarnews/20...

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