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A $(t,n)-$ threshold signature scheme enables distributed signing among $n$ players such that any subgroup of size $t$ can sign, whereas any group with fewer players cannot. Our goal is to produce signatures that are compatible with an existing centralized signature scheme: the key generation and signature algorithm are replaced by a communication protocol between the parties, but the verification algorithm remains identical to that of a signature issued using the centralized algorithm. Starting from the threshold schemes for the ECDSA signature due to R. Gennaro and S. Goldfeder, we present the first protocol that supports multiparty signatures with an offline participant during the Key Generation Phase, without relying on a trusted third party. Following well-established approaches, we prove our scheme secure against adaptive malicious adversaries.
Blockchain-based cryptocurrencies, facilitating the convenience of payment by providing a decentralized online solution, have not been widely adopted so far due to slow confirmation of transactions. Offline delegation offers an efficient way to excha
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework consists of
In this work, we study how to securely evaluate the value of trading data without requiring a trusted third party. We focus on the important machine learning task of classification. This leads us to propose a provably secure four-round protocol that
An increasing number of businesses are replacing their data storage and computation infrastructure with cloud services. Likewise, there is an increased emphasis on performing analytics based on multiple datasets obtained from different data sources.
Accurate identification of effective epidemic threshold is essential for understanding epidemic dynamics on complex networks. The existing studies on the effective epidemic threshold of the susceptible-infected-removed (SIR) model generally assume th