Apple shares recordings and research from recent privacy-focused AI and ML workshop

Apple has published four recordings and a research recap from its 2026 Workshop on Privacy-Preserving Machine Learning & AI. Here are the details.

Apple publishes videos on ML and privacy workshop

Apple has published a new post on its machine learning blog with four featured talks from its 2026 Workshop on Privacy-Preserving Machine Learning & AI.

In this two-day event, Apple researchers and members from the broader research community discussed “the latest in privacy-preserving ML and AI,” focusing on Private Learning and Statistics, Foundation Models and Privacy, and Attacks and Security.

Here’s Apple on the event:

Presentations and discussions at the workshop explored advances and open questions in privacy and ML, including federated learning, statistical learning, trust models, attacks, privacy accounting, and the unique challenges presented by foundation models. These research areas ground innovation in rigorous privacy and security evaluation, bridging theoretical frameworks with real-world applications.

In its blog post, Apple featured four talks, including the ‘Crypto for DP and DP for Crypto’ presentation, given by the company’s Research Scientist Kunal Talwar.

You can watch it below:

Additionally, other featured talks include:

• Online Matrix Factorization and Online Query Release, presented by University of Toronto’s Aleksandar Nikolov
• Learning from the People: Communicating about S&P Technology for Responsible Data Collection, presented by Georgetown’s Elissa Redmiles
• Understanding and Mitigating Memorization in Foundation Models – presented by CISPA’s Franziska Boenisch

Apple also highlighted 24 published works presented at the workshop, including three papers developed by current and former researchers at the company:

• Combining Machine Learning and Homomorphic Encryption in the Apple Ecosystem
• Efficient privacy loss accounting for subsampling and random allocation
• Trade-offs in Data Memorization via Strong Data Processing Inequalities

To watch all the sessions and see the full list of referenced papers, follow this link.

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