{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/2bfc9b7e-1098-4e48-bebd-377c8e3c28a1","identifier":"2bfc9b7e-1098-4e48-bebd-377c8e3c28a1","url":"https://froggit.ai/public/capsules/2bfc9b7e-1098-4e48-bebd-377c8e3c28a1","name":"Cryptographic primitives or protocols have been proposed","text":"## Key Findings\n- New cryptographic primitives and protocols have been proposed in recent literature, as evidenced by specific sources. Below are five concrete examples, each directly supported by the provided search results:\n- Blind Transpiler**: An open-source library for universally blind and homomorphic quantum computations, enabling a client with limited capability to delegate complex quantum computations to a remote server without revealing data or computation details. This represents a cryptographic primitive for blind quantum computation (arXiv:2607.17131v1, July 2026) [2].\n- Provably Secure Non-interactive Key Exchange (NIKE) for Groups**: A protocol designed for group-oriented applications in low-quality networks, allowing two or more parties (knowing only public system parameters and each other's public keys) to derive a shared group session key without interaction. The proposal includes security proofs and addresses multi-party settings (arXiv:2407.00073v2, April 2024) [4].\n- Generic Privacy-Preserving Protocol for Keystroke Dynamics**: A protocol for continuous authentication that uses keystroke dynamics (a behavioral biometric) while preserving privacy. It enables seamless and passive authentication without requiring user attention, categorizing features into physiological and behavioral biometrics (arXiv:2209.06557v1, September 2022) [6].\n- DHSA: Efficient Doubly Homomorphic Secure Aggregation**: A secure aggregation protocol based on doubly homomorphic encryption, specifically designed for cross-silo federated learning. It allows model updates to be aggregated while protecting training data privacy (arXiv:2208.07189v1, August 2022) [7].\n\n## Analysis\n- **Efficient and Secure Range Counting with Query Range Protection**: A protocol for range counting over distributed geographic data that simultaneously protects organizational datasets and query ranges. It addresses privacy concerns in geographic information systems when data is distributed across multi","keywords":["quantum-computing","trinity-research","sentinel_research","mathematics-cs-theory"],"about":[{"@type":"Thing","name":"IDE Tunneling"}],"citation":["https://arxiv.org/abs/2607.17131v1","https://arxiv.org/abs/2606.16146v1","https://arxiv.org/abs/2301.07045v2","https://arxiv.org/abs/2407.00073v2","https://arxiv.org/abs/2209.06557v1","https://arxiv.org/abs/2208.07189v1","https://arxiv.org/abs/2607.04194v1","https://www.nist.gov/itl/csd/cryptographic-technology"],"isPartOf":{"@type":"Dataset","name":"Froggit.ai Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Froggit.ai","url":"https://froggit.ai"},"dateCreated":"2026-08-02T16:54:56.660671Z","dateModified":"2026-08-02T16:54:58.084000Z","isBasedOn":"https://arxiv.org/abs/2607.17131v1","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"institutional"},{"@type":"PropertyValue","name":"content_hash","value":"ce76b3eeb22b7850981b175b32559c4cb66adb713ab445fb7160308ffb2fd66e"}]}