One of the most fundamental notions of cryptography is that of simulation. It stands behind the concepts of semantic security, zero knowledge, and security for multiparty computation. However, writing a simulator and proving security via the use of simulation is a non-trivial task, and one that many newcomers to the field often find difficult. In this tutorial, we provide a guide to how to writ...

As an important branch of quantum secure multiparty computation, quantum private comparison (QPC) has attracted more and more attention recently. In this paper, according to the quantum implementation mecha‐ nism that these protocols used, we divide these protocols into three categories: The quantum cryptography QPC, the superdense coding QPC, and the entanglement swapping QPC. And then, a more...

Many simple calculations can be done easier without computers than by using them. We show that the same holds for secure multiparty computations if the function to be computed is simple enough. Our starting point is an observation of Bert den Boer: a multiparty computation of a logical ANDgate can be performed by ve simple playing cards. We show that by using a reasonable amount of cards many u...

Privacy-preserving techniques for distributed computation have been proposed recently as a promising framework in collaborative inter-domain network monitoring. Several different approaches exist to solve such class of problems, e.g., Homomorphic Encryption (HE) and Secure Multiparty Computation (SMC) based on Shamir’s Secret Sharing algorithm (SSS). Such techniques are complete from a computat...

We show how to collect and analyze financial data for a consortium of ICT companies using secret sharing and secure multiparty computation (MPC). This is the first time where the actual MPC computation on real data was done over the internet with computing nodes spread geographically apart. We describe the technical solution and present user feedback revealing that MPC techniques give sufficien...

Vaidya, Jaideep Shrikant. Ph.D., Purdue University, August, 2004. Privacy Preserving Data Mining over Vertically Partitioned Data. Major Professor: Chris Clifton. The goal of data mining is to extract or “mine” knowledge from large amounts of data. However, data is often collected by several different sites. Privacy, legal and commercial concerns restrict centralized access to this data. Theore...

We propose a four-tiered parallelization model for acceleration of the secure multiparty computation (SMC) on the CUDA based Graphic Processing Unit (GPU) cluster architecture. Specification layer is the top layer, which adopts the SFDL of Fairplay for specification of secure computations. The SHDL file generated by the SFDL compiler of Fairplay is used as inputs to the function layer, for whic...

Using homomorphic encryption and secure multiparty computation, cloud servers may perform regularly structured computation on encrypted data, without access to decryption keys. However, prior approaches for programming on encrypted data involve restrictive models such as boolean circuits, or standard languages that do not guarantee secure execution of all expressible programs. We present an exp...

This paper provides an introduction and overview to mystery values, which are analogues to eigenvalues that arise in the [KWG10] model of secure multiparty computation. Essentially, most of the paper serves to answer, and elaborate on, the question “when can n people simulate a fair coin flip?”

We present several techniques that aim to compute the belief or knowledge a party might have about the values of hidden variables involved in the computation. These techniques can be used for enforcing knowledge-based security policies and for optimizing secure multiparty computations.