Private computation is a field of study focused on enabling computations to be performed on data without exposing the data itself. This is achieved through various cryptographic techniques that allow computations to be executed while maintaining the confidentiality of the inputs and intermediate results. It is essential for scenarios requiring data analysis where privacy is a primary concern.
Context
The integration of private computation techniques into blockchain and AI systems is a significant area of development, aiming to balance transparency with data confidentiality. Discussions often highlight the challenges in achieving practical scalability and efficiency for these methods, their application in areas like secure data marketplaces and privacy-preserving machine learning, and the potential for them to unlock new use cases for sensitive information.
ZKPoT consensus leverages zk-SNARKs to cryptographically verify decentralized model training performance, ensuring data privacy and robust, efficient machine learning on-chain.
A novel ZKP system, zkLLM, enables the efficient, private verification of 13-billion-parameter LLM outputs, securing AI integrity and intellectual property.
This new cryptographic primitive enables distributed authorities to generate a single, succinct decryption key for an arbitrary batch of identities at a cost independent of the batch size, fundamentally solving key management scalability in threshold systems.
ZKPoT, a novel zk-SNARK-based consensus, verifies decentralized machine learning contributions without exposing private data, ensuring both efficiency and privacy.
A Zero-Knowledge Proof of Training consensus mechanism leverages zk-SNARKs to enable private, verifiable model contributions, securing decentralized AI computation.
HyperPlonk introduces a new polynomial commitment scheme, achieving a universal and updatable setup with dramatically faster linear-time proving, enabling mass verifiable computation.
Silently verifiable proofs introduce a cryptographic primitive that reduces batch verification communication overhead to a single field element, unlocking truly scalable private computation.
Research introduces ZKPoT consensus, leveraging zk-SNARKs to cryptographically verify machine learning contributions without exposing private training data or model parameters.
This research introduces novel ZK arguments for the CL cryptosystem, enabling private, verifiable computations in unknown order groups for enhanced privacy.
A novel Private Set Intersection protocol leverages one-sided permutations, fundamentally advancing secure data collaboration by optimizing privacy and computational efficiency for asymmetric datasets.
A novel framework enables third-party computation and efficient set updates for private set intersection, expanding its utility in dynamic, privacy-preserving distributed systems.
This research introduces a framework for smart contracts that ensures both on-chain and off-chain data privacy, enabling secure and anonymous decentralized applications.
A novel cryptographic framework enables secure, private, and scalable decentralized computation by eliminating reliance on traditional blockchain consensus mechanisms.
A novel framework leverages zero-knowledge proofs to enable verifiable, private execution of economic mechanisms without revealing their underlying rules or requiring trusted intermediaries.
Researchers have refined indistinguishability obfuscation, enabling it to rely solely on the standard Learning With Errors assumption, promising more robust and practical privacy-preserving cryptographic primitives.
This research pioneers decentralized, verifiable multiparty computation for generative AI, safeguarding user privacy and model integrity against centralized control.
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