Secure multiparty computation (MPC) permits a collection of parties to compute a collaborative result without any of the parties or compute servers gaining any knowledge about the inputs provided by other parties, except what can be determined from the output of the computation. In the form of MPC known as linear (or additive) sharing, computation proceeds on data that appears entirely random. ...

In 1982, Bennett and Brassard suggested a new way to provide privacy in long distance communications with security based on the correctness of the basic principles of quantum mechanics. The scheme allows two parties, Alice and Bob, sharing no secret information in the first place, to exchange messages that nobody else can figure out. The only requirement is a quantum channel and a normal phone ...

Secure sum computation of private data inputs is an interesting example of Secure Multiparty Computation (SMC) which has attracted many researchers to devise secure protocols with lower probability of data leakage. In this paper, we provide a novel protocol to compute the sum of individual data inputs with zero probability of data leakage when two neighbor parties collude to know the data of a ...

Secure computation enables mutually suspicious parties to compute a joint function of their private inputs while providing strong security guarantees. Amongst other things, even if some of the participants are corrupted the output is still correctly computed, and parties do not learn anything about each other’s inputs except for that output. Despite the power and generality of secure computatio...

In secure multi-party computation n parties jointly evaluate an n-variate function f in the presence of an adversary which can corrupt up till t parties. All honest parties are required to receive their correct output values, irrespective of how the corrupted parties under the control of the adversary behave. The adversary should not be able to learn anything more about the input values of the ...

secure multi-party computation is widely studied area in computer science. It is touching all most every aspect of human life. This paper demonstrates theoretical and experimental results of one of the secure multi-party computation protocols proposed by Shukla et al. implemented using visual C++. Data outflow probability is computed by changing parameters. At the end, time and space complexity...

We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, party (Alice) holds message, while another (Bob) classifier. At end protocol, Alice will only learn result applied her input Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Rust implementation provides fast secure for classificatio...