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Apple Workshop on Privacy-Preserving Machine Learning & AI 2026 - Apple ...
content type eventpublished May 8, 2026Apple ML researcher Kunal Talwar presenting at the workshop.Apple ML researcher Kunal Talwar presenting at the workshop.At Apple, we believe privacy is a fundamental human right. As AI capabilities increase and become more integrated into people’s daily lives, advancing research in privacy-preserving techniques is increasingly important to ensure privacy is protected while users enjoy innovative AI experiences. Apple’s fundamental research has consistently pushed the state-of-the-art in this domain, and earlier this year, we hosted the Workshop on Privacy-Preserving Machine Learning & AI. This two-day event brought together Apple researchers and members of the broader research community to discuss the latest in privacy-preserving ML and AI, focusing on three key areas: Private Learning and Statistics, Foundation Models and Privacy, and Attacks and Security. 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 this post, we share recordings of selected talks and a recap of the publications discussed at the workshop. Crypto for DP and DP for Crypto - presented by Kunal Talwar Online Matrix Factorization and Online Query Release - presented by Aleksandar Nikolov (University of Toronto) Learning from the People: Communicating about S&P Technology for Responsible Data Collection - presented by Elissa Redmiles (Georgetown) Understanding and Mitigating Memorization in Foundation Models - presented by Franziska Boenisch (CISPA) Adaptive Methods Are Preferable in High Privacy Settings: An SDE Perspective by Enea Monzio Compagnoni (University of Basel), Alessandro Stanghellini (University of Basel), Rustem Islamov (University of Basel), Aurelien Lucchi (University of Basel), and Anastasiia Koloskova (University of Zurich) Captured by Captions: On Memorization and its Mitigation in Clip Models by Wenhao Wang (CISPA), Adam Dziedzic (CISPA), Grace C. Kim (Georgia Institute of Technology), Michael Backes (CISPA), and Franziska Boenisch (CISPA) Combining Machine Learning and Homomorphic Encryption in the Apple Ecosystem by Apple researchers Concurrent Composition for Differentially Priv...
Apple Workshop Shows ML with Homomorphic Encryption,...
Editorial illustration for Apple Workshop Shows ML with Homomorphic Encryption, Georgia Institute, CISPA Apple frames privacy as a basic human right, and that stance shapes its AI research agenda. As machine‑learning tools seep deeper into everyday apps, the company says protecting user data can’t be an afterthought. Earlier this year it convened a two‑day Workshop on Privacy‑Preserving Machine Learning & AI, pulling together Apple engineers and external scholars to hash out what comes next. The program zeroed in on three strands: Private Learning and Statistics, Foundation Models and Privacy, and Attacks and Security. While some sessions unpacked federated learning and statistical‑learning theory, others tackled trust models, privacy accounting and the thorny vulnerabilities of large‑scale foundation models. The talks didn’t just stay in the lab; they linked rigorous theory to the kinds of deployments Apple rolls out to millions. In the post‑event release, Apple shares recordings and a rundown of the papers discussed, spotlighting work such as “Adaptive Methods Are Preferable in High Privacy Settings: An SDE Perspective” from researchers at the University of Basel and Zurich, and “Captured by Captions: On Memorization and its Mitigation in CLIP Models” from CISPA and the University of Waterloo. Why this matters Apple's recent workshop on privacy‑preserving machine learning signals a clear intent to embed homomorphic encryption and differential privacy deeper into its ecosystem, a move that could shape how developers design AI features for iOS and macOS. We note the involvement of academics from Georgia Tech, CISPA and the Institute of Science, suggesting the research is not purely internal but benefits from external expertise. Yet, the summary offers no details on performance trade‑offs, scalability or timelines for integration, leaving it uncertain whether the techniques will be practical for everyday apps. Can we expect these methods to ship soon? For founders, the promise of stronger privacy guarantees may lower regulatory risk, but the cost of implementing homomorphic encryption could be prohibitive without clear benchmarks. Researchers can see Apple’s commitment as an invitation to explore more efficient algorithms, though the workshop’s outcomes remain to be validated in production. In short, the initiative underscores the growing tension between AI innovation and privacy, and we’ll be watching how Apple translates these concepts into usable too...
Workshop on Privacy in NLP - ACL Anthology
2021 Proceedings of the Third Workshop on Privacy in Natural Language Processing 8 papers
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