Secure Computation refers to cryptographic techniques that allow multiple parties to jointly compute a function over their inputs while keeping those inputs private. This ensures that no party learns anything about the other parties’ inputs beyond what can be inferred from the output of the computation itself. It is foundational for privacy-preserving applications in decentralized systems.
Context
Secure computation techniques, such as multi-party computation (MPC) and homomorphic encryption, are gaining traction for their ability to enable private data analysis and secure transactions on blockchains. News frequently highlights new applications of these technologies in areas like decentralized identity, private voting, and confidential DeFi transactions. Ongoing research aims to improve the performance and scalability of these cryptographic primitives for broader adoption.
This research introduces Collaborative zk-SNARKs, a cryptographic primitive allowing distributed parties to prove a statement about their collective secret data without centralization, achieving near-single-prover efficiency.
We generalize MPC-in-the-head to create post-quantum zero-knowledge arguments, securing verifiable computation against quantum superposition attacks using LWE.
This research introduces Zero-Knowledge Proof of Training, a zk-SNARK-based consensus mechanism that validates machine learning contributions without compromising participant data privacy, enabling secure, scalable decentralized AI.
This breakthrough uses Homomorphic Encryption to perform biometric verification directly on encrypted data, enabling a provably private and secure decentralized identity layer.
Research introduces ZKPoT consensus, leveraging zk-SNARKs to cryptographically verify machine learning contributions without exposing private training data or model parameters.
This research introduces verifiable one-time programs, foundational for a novel single-round secure computation model, unlocking practical quantum-assisted cryptography with minimal resources.
This research introduces verifiable one-time programs, enabling single-round secure computation with minimal quantum resources, accelerating practical quantum internet applications.
This research introduces verifiable one-time programs, unlocking secure, single-round quantum-assisted computation for critical blockchain and internet applications.
Nil Message Compute introduces a cryptographic framework for secure, private, and scalable decentralized computation, transcending traditional blockchain limitations.
Silentflow pioneers TEE-assisted MPC, eliminating communication bottlenecks in Correlated Oblivious Transfer for real-time edge inference, advancing privacy-preserving computation.
A novel framework integrates post-quantum cryptography with blockchain to fortify federated learning against quantum threats, ensuring long-term data security.
ZKP technology enables verifiable computation without revealing underlying data, fundamentally transforming privacy and integrity across decentralized and traditional systems.
This research optimizes threshold ECDSA by leveraging homomorphic encryption, enabling robust, efficient distributed signing with reduced communication overhead for decentralized applications.
This research introduces a zk-SNARK-based framework for private smart contracts on permissioned blockchains, enabling secure, decentralized transactions like Delivery vs. Payment.
This research pioneers protocols leveraging secure computation and zero-knowledge proofs to enable fair, scalable, and private blockchain applications.
This research introduces a novel cryptographic primitive enabling private set intersection where one party learns the common elements succinctly, without revealing their own set.
New protocols drastically reduce communication overhead in secure multi-party computation with selective aborts, enhancing practicality for decentralized applications.
This research proves witness encryption is essential for highly secure, randomness-reusable zero-knowledge arguments, advancing practical privacy solutions.
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