Secure multiparty computation (MPC) is perhaps the most popular paradigm in the area of cryptographic protocols. It allows several mutually untrustworthy parties to jointly compute a function of their private inputs, without revealing to each other information about those inputs. In the case of unconditional (information-theoretic) security, protocols are known which tolerate a dishonest minori...

Adaptively secure multiparty computation is an essential and fundamental notion in cryptography. In this work we focus on the basic question of constructing a multiparty computation protocol secure against a malicious, adaptive adversary in the stand-alone setting without assuming an honest majority, in the plain model. It has been believed that this question can be resolved by composing known ...

We propose several variants of a secure multiparty computation protocol for AES encryption. The best variant requires 2200 + 400 255 expected elementary operations in expected 70 + 20 255 rounds to encrypt one 128-bit block with a 128-bit key. We implemented the variants using VIFF, a software framework for implementing secure multiparty computation (MPC). Tests with three players (passive secu...

Secure multiparty computation considers the problem of different parties computing a joint function of their separate, private inputs without revealing any extra information about these inputs than that is leaked by just the result of the computation. This setting is well motivated, and captures many different applications. Considering some of these applications will provide intuition about how...

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...

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 ...

Federated learning (FL) is a privacy-aware data mining strategy keeping the private on owners’ machine and thereby confidential. The clients compute local models send them to an aggregator which computes global model. In hybrid FL, parameters are additionally masked using secure aggregation, such that only aggregated statistics become available in clear text, not client specific updates. this c...

We present an efficient implementation of the Orlandi protocol which is the first implementation of a protocol for multiparty computation on arithmetic circuits, which is secure against up to n−1 static, active adversaries. An efficient implementation of an actively secure selftrust protocol enables a number of multiparty computation where one or more of the parties only trust himself. Examples...

Introduced by Yao in early 1980s, secure computation is being one among the major area of research interest among cryptologists. In three decades of its growth, secure computation which can be called as two-party computation, or multiparty computation depending on the number of parties involved has experienced vast diversities. Research has been carried out by exploiting specific properties of ...