Conference on Secure and Trustworthy Machine Learning
@satml.org
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IEEE Conference on Secure and Trustworthy Machine Learning March 2026 (Munich) • #SaTML2026 https://satml.org/
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Got some hot research cooking? 🔥

The @satml.org paper deadline is just 9 days away. We are looking forward to your work on security, privacy, and fairness in machine learning.

👉 satml.org/call-for-pap...
⏰ Sep 24
Reposted by Conference on Secure and Trustworthy Machine Learning
rieck.mlsec.org
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
Reposted by Conference on Secure and Trustworthy Machine Learning
rieck.mlsec.org
📣 Researchers in AI security, privacy & fairness: It's time to share your latest work!

The SaTML 2026 submission site is live 👉 hotcrp.satml.org

🗓️ Deadline: Sept 24, 2025

@satml.org
SaTML 2026
hotcrp.satml.org
Reposted by Conference on Secure and Trustworthy Machine Learning
rieck.mlsec.org
🚨 Got a great idea for an AI + Security competition?

@satml.org is now accepting proposals for its Competition Track! Showcase your challenge and engage the community.

👉 satml.org/call-for-com...
🗓️ Deadline: Aug 6
Reposted by Conference on Secure and Trustworthy Machine Learning
rieck.mlsec.org
We’re happy to announce the Call for Competitions for
@satml.org

The competition track has been a highlight of SaTML, featuring exciting topics and strong participation. If you’d like to host one for SaTML 2026, visit:

👉 satml.org/call-for-com...
⏰ Deadline: Aug 6
Call for Competitions
Competition proposal deadline: August 6, 2025
Decision notification: August 27, 2025
Reposted by Conference on Secure and Trustworthy Machine Learning
rieck.mlsec.org
We're excited to announce the Call for Papers for SaTML 2026, the premier conference on secure and trustworthy machine learning @satml.org

We seek papers on secure, private, and fair learning algorithms and systems.

👉 satml.org/call-for-pap...
⏰ Deadline: Sept 24
IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), March 23-25, 2025, Munich

Submission deadline: September 24, 2025
satml.org
🎤 That’s a wrap on #SaTML25! Huge thanks to the speakers, organizers, reviewers, and everyone who joined the conversation. See you next time!
satml.org
🔍 How private was that release? @a-h-koskela.bsky.social presents a method for auditing DP guarantees using density estimation. #SaTML25
satml.org
🧮 Getting the math right. @matt19234.bsky.social walks through common traps in privacy accounting and how to avoid them. #SaTML25
satml.org
🧠 Marginals leak. Steven Golob shows how synthetic data built on marginals can still compromise privacy. Paper: arxiv.org/abs/2410.05506 #SaTML25
satml.org
📃🔐 Privacy and fairness? Khang Tran introduces FairDP, enabling fairness certification alongside differential privacy. Paper: arxiv.org/abs/2305.16474 #SaTML25
satml.org
📏 Wrapping up the talks with deep dives into differential privacy—Session 14 gets technical, from fairness to auditing.
satml.org
🖼️📡 Hide and seek. Luke Bauer presents a method for covert messaging with provable security via image diffusion. Paper: arxiv.org/abs/2503.10063 #SaTML25
satml.org
💣 Still work to do. Yigitcan Kaya makes the case that ML-based behavioral malware detection is fragile and far from solved. Paper: arxiv.org/abs/2405.06124 #SaTML25
satml.org
🕵️‍♂️ From detection to covert messaging—Session 13 explores the gray areas of ML security. #SaTML25
satml.org
💻 What can you learn privately when compute is tight? Zachary Charles tackles user-level privacy under realistic constraints. #SaTML25
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📊 Not all public datasets are equal. Xin Gu proposes a new metric—gradient subspace distance—to guide private learning choices. Paper: arxiv.org/abs/2303.01256 #SaTML25
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📚🔒 Choose wisely. Kristian Schwethelm presents a method to balance data utility and privacy in active learning. Paper: arxiv.org/abs/2410.00542 #SaTML25
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⚖️ Privacy isn’t always fair. Kai Yao breaks down the mechanisms that can introduce unfairness into private learning. Paper: arxiv.org/abs/2501.14414 #SaTML25
satml.org
🔐 Starting the final afternoon at #SaTML25 with Session 12—private learning from all angles: fairness, dataset selection, active learning, and budget-aware privacy.
satml.org
🌲💀 Even decision trees aren’t safe. Lorenzo Cazzaro shows how to poison tree-based models. Paper: arxiv.org/abs/2410.00862 #SaTML25
satml.org
🚗🔦 How robust are LiDAR detectors?Alexandra Arzberger presents Hi-ALPS, benchmarking six systems used in autonomous vehicles. Paper: arxiv.org/abs/2503.17168 #SaTML25
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🎯 Robustness meets domain adaptation. Natalia Ponomareva introduces DART, a principled method for adapting without labels—and withstanding attacks. #SaTML25