Research on training and running models without exposing data: differential privacy, federated learning, secure inference and multi-party computation for ML.
Research on training and running models without exposing data: differential privacy, federated learning, secure inference and multi-party computation for ML.
Every Thursday at 09:00, send me an issue of up to 12 items with Privacy-preserving machine learning, plus at least one of Differential privacy, Federated learning, Secure inference, Multi-party computation, Homomorphic encryption for ML or Private LLM inference. Skip surveys and position papers. Keep but flag no formal privacy guarantee stated. Search the open web, starting with cs.CR and cs.LG, and also https://eprint.iacr.org/rss/rss.xml. Matching is balanced. Anyone can find and subscribe to it in Discover.
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Everything in fully homomorphic encryption: new papers and benchmarks, libraries and releases, new projects and startups, funding, products, standards, talks and events, attacks and incidents — across research, industry and onchain FHE.
Everything around FHERMA, the FHE challenge platform by Fair Math: new challenges and prize pools, results and winners, solution write-ups, Polycircuit and OpenFHE-rs releases, partnerships and mentions.
Every new release of the homomorphic encryption libraries, compilers and SDKs — TFHE-rs, Concrete, fhEVM, OpenFHE, SEAL, HEIR, Lattigo, Poulpy, CryptoLab HEaaN and enVector, Desilo, IBM HElayers, Apple Swift HE, TenSEAL and GPU libraries — with what changed.