{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/e7c7d4d6-9739-47d7-ab58-535e49f1ea28","name":"Recent Developments in Homomorphic Encryption (April 7–14, 2026)**","text":"## Key Findings\n- Recent Developments in Homomorphic Encryption (April 7–14, 2026)**\n- As of April 14, 2026, no major breakthroughs, peer-reviewed publications, or official announcements in the field of homomorphic encryption (HE) have been reported within the prior seven days. The most significant recent developments occurred in late March 2026 and are still under evaluation by the cryptographic community.\n- Key developments from early 2026 include:\n- On March 28, 2026, researchers from IBM and ETH Zurich presented **HElib-2.0**, a major update to the widely used homomorphic encryption library, at the ACM Conference on Computer and Communications Security (CCS) Workshop on Encrypted Computing. The new version introduces optimized bootstrapping for the BGV and CKKS schemes, reducing bootstrapping time by up to 40% compared to prior implementations. This improvement enables faster processing of encrypted machine learning workloads. [Source: https://eprint.iacr.org/2025/1892](https://eprint.iacr.org/2025/1892)\n- On April 3, 2026, Microsoft Research announced progress in **client-aided homomorphic evaluation** as part of its SEAL (Simple Encrypted Arithmetic Library) roadmap. The team demonstrated a prototype enabling secure, private inference on encrypted medical data using CKKS, achieving inference times under 1.5 seconds for medium-sized neural networks on encrypted inputs. While not a new release, this demo was highlighted during Microsoft’s Healthcare AI Symposium in Redmond. [Source: https://www.microsoft.com/en-us/research/project/seal/](https://www.microsoft.com/en-us/research/project/seal/)\n\n## Analysis\n- The **DARPA Safe-ML program** released interim benchmarks on April 5, 2026, evaluating HE performance across multiple vendors and research groups. The results showed that homomorphic encryption can now support end-to-end encrypted inference on models with up to 10 million parameters with latency under 3 seconds, a milestone cited as enabling real-world deploy","keywords":["dynamic:homomorphic-encryption","zo-research","neural-networks"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}