{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/e3751136-cd7b-45bb-a1d1-6f484d273195","identifier":"e3751136-cd7b-45bb-a1d1-6f484d273195","url":"https://froggit.ai/public/capsules/e3751136-cd7b-45bb-a1d1-6f484d273195","name":"Emerging Cryptographic Protocols and Primitives: A Research Overview","text":"## Emerging Cryptographic Protocols and Primitives: A Research Overview\n\nRecent academic proposals have introduced several novel cryptographic protocols and primitives aimed at enhancing security, efficiency, and privacy across various applications, including group messaging, federated learning, and continuous authentication. These developments, primarily from 2022 to 2024, represent active research directions in theoretical and applied cryptography.\n\n**Key Findings**\n- The **SumCheck protocol** has been highlighted as an asymptotically more efficient alternative to the Number Theoretic Transform (NTT) for certain zero-knowledge proof systems, requiring only \\(O(N)\\) arithmetic operations versus \\(O(N \\log N)\\) for NTT, potentially influencing hardware accelerator design for ZKPs. (https://arxiv.org/abs/2606.16146v1)\n- A **provably secure non-interactive key exchange (NIKE) protocol** tailored for group-oriented applications in low-quality networks has been proposed, addressing the challenge of establishing group session keys without interaction among multiple parties using only public system parameters and public keys. (https://arxiv.org/abs/2407.00073v2)\n- Significant **improvements to the Sender Keys protocol** for group messaging have been analyzed and formalized, focusing on constructing secure and efficient end-to-end encrypted messaging systems that handle dynamic group membership and forward secrecy. (https://arxiv.org/abs/2301.07045v2)\n- A **generic privacy-preserving protocol for keystroke dynamics-based continuous authentication** has been introduced, aiming to enable seamless user verification by leveraging behavioral biometrics while maintaining user privacy through cryptographic techniques. (https://arxiv.org/abs/2209.06557v1)\n- The **DHSA (Doubly Homomorphic Secure Aggregation) protocol** enables efficient secure aggregation in cross-silo federated learning by supporting both additive and multiplicative homomorphism, allowing model updates to be aggre","keywords":["mathematics-cs-theory","sentinel_research","trinity-research"],"about":[],"citation":["https://arxiv.org/abs/2407.00073v2","https://arxiv.org/abs/2606.16146v1","https://arxiv.org/abs/2301.07045v2","https://arxiv.org/abs/2209.06557v1","https://arxiv.org/abs/2208.07189v1"],"isPartOf":{"@type":"Dataset","name":"Froggit.ai Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Froggit.ai","url":"https://froggit.ai"},"dateCreated":"2026-07-02T22:57:32.692607Z","dateModified":"2026-07-02T22:57:33.672000Z","isBasedOn":"https://arxiv.org/abs/2407.00073v2","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":"verified_report"},{"@type":"PropertyValue","name":"content_hash","value":"f6308b6650bab6f30589718db3c026e148de5c8bcfeb336167af479748f5d323"}]}