New exciting and timely Trustworthy AI Seminar tomorrow by Ilia Shumailov (AI Sequrity Company) ** What is going on with prompt injections? ** Tomorrow (Friday, May 1) at 9am PDT / 12pm EDT / 6pm CEST Details and Zoom/YouTube links at trust-ai-sem.github.io

Trustworthy AI Seminar Series

Trustworthy AI Seminar Series

trust-ai-sem.github.io

New exciting Trustworthy AI Seminar this Friday! Earlence Fernandes (UC San Diego) will present "The systems security approach to AI agents." Details and Zoom/YouTube stream link on our website : trust-ai-sem.github.io

Trustworthy AI Seminar Series

Trustworthy AI Seminar Series

trust-ai-sem.github.io

I'm excited to teach a Cybersafety class this quarter at UCR. In this first iteration, it'll be offered as a seminar class, but we will switch to a regular class next year. Preliminary syllabus: spalab.cs.ucr.edu/teaching/26s... Looking forward to comments and feedback!

🎙️ New Trustworthy AI Seminar ⏰ April 3 | 9am PDT / 12pm EDT / 6pm CEST 🗣️Sahar Abdelnabi ( @sahar-abdelnabi.bsky.social): "Stateless Yet Not Forgetful: Implicit Memory as a Hidden Channel in LLMs" LLMs can carry hidden state across sessions — with zero external memory! 👉 trust-ai-sem.github.io

Trustworthy AI Seminar Series

Trustworthy AI Seminar Series

trust-ai-sem.github.io

Congrats to Sundar -- his paper "To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling" was just accepted to #NDSS2026! Only 21 out of 950 straight accepts this round 😳 Joint work with Borja Balle, Jamie Hayes, & @emilianodc.com Pre-print: arxiv.org/abs/2411.10614

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling

The Differentially Private Stochastic Gradient Descent (DP-SGD) algorithm allows the training of machine learning (ML) models with formal Differential Privacy (DP) guarantees. Since DP-SGD processes t...

arxiv.org

📣 Upcoming CCS'25 paper 📣 The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data We investigate the critical yet overlooked role of discretization in end-to-end differentially private (DP) tabular synthetic data generation

Conditional congrats to Georgi and Sundar - their paper on Discretization in DP Synthetic Data was accepted with shepherding to CCS’25. Very important work on an overlooked aspect of end-to-end DP pipelines. Preprint: arxiv.org/abs/2504.06923 -- feedback welcome!

The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data

Differentially Private (DP) generative marginal models are often used in the wild to release synthetic tabular datasets in lieu of sensitive data while providing formal privacy guarantees. These model...

arxiv.org

Huge congrats to @spalab.cs.ucr.edu's Georgi Ganev for receiving the Distinguished Paper Award at IEEE S&P for his work "The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against “Truly Anonymous” Synthetic Datasets." Paper: arxiv.org/pdf/2312.051...

Happy to announce that Georgi's paper, “The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against “Truly Anonymous” Synthetic Datasets,” has been accepted to IEEE Security & Privacy. Pre-print: arxiv.org/pdf/2312.05114

I’m proverbially humbled and manifestly honored by the ACM’s recognition as Distinguished Member. This is obviously an achievement that would have never been possible without my amazing students and collaborators or the support and mentorship of so many people. www.acm.org/media-center...

ACM has named 56 Distinguished Members for work that has advanced computing, fostered innovation across various fields and improved computer science education.

The 2024 ACM Distinguished Members work at leading universities, corporations, and research institutions in Australia, Bangladesh, Canada, China, Cyprus, Denmark, Germany, Ireland, India, Switzerland,...

acm.org