Fast Multiparty Threshold ECDSA with Fast Trustless Setup
A threshold signature scheme enables distributed signing among n players such that any subgroup of size t + 1 can sign, whereas any group with t or fewer players cannot. While there exist previous threshold schemes for the ECDSA signature scheme, we present the first protocol that supports multiparty signatures for any t ≤ n with efficient, dealerless key generation. Our protocol is faster than previous solutions and significantly reduces the communication complexity as well. We prove our scheme secure against malicious adversaries with a dishonest majority. We implemented our protocol, demonstrating its efficiency and suitability to be deployed in practice.
Code (1)
Similar Papers 제목 키워드 기반
Self-Supervised Inference of Agents in Trustless Environments
In this paper, we propose a novel approach where agents can form swarms to produce high-quality responses effectively. This is accomplished by utilizing agents capable of data inference and ranking, which can be effectiv…
Fast When, Careful Who: Dual-Process Multiparty Turn-Taking with Diffusion Augmentation
Reliable turn-taking is essential for spoken dialogue systems. However, most existing methods are designed for two-speaker interaction and struggle with realistic multiparty audio containing overlap and rapid speaker cha…
Data AugmentationFast Privacy-Preserving Text Classification based on Secure Multiparty Computation
We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At t…
ClassificationGeneral ClassificationPrivacy PreservingSpam detection+2Towards ECDSA key derivation from deep embeddings for novel Blockchain applications
In this work, we propose a straightforward method to derive Elliptic Curve Digital Signature Algorithm (ECDSA) key pairs from embeddings created using Deep Learning and Metric Learning approaches. We also show that these…
Deep LearningMetric LearningProof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains
Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated powerful and disruptive capabilities in natural language and image tasks. However, deploying these models in decentralized environments remains …
CPU