Secure Multiparty Computation technique plays an important role in the security research of distributed systems.This paper firstly summarizes the advances in theoretical and practical studies on SMC,and then analysis the basic protocols and its related cryptographic algorithms.Secondly,the key tools,secret share,homomorphic public key cryptography,mix network,zero knowledge proof,oblivious transfer and private comparison protocol in implementation SMC are studied deeply.Finally,some research directions in this area are also proposed.
Secure Multiparty Computation technique plays an important role in the security research of distributed systems.This paper firstly summarizes the advances in theoretical and practical studies on SMC,and then analysis the basic protocols and its related cryptographic algorithms.Secondly,the key tools,secret share,homomorphic public key cryptography,mix network,zero knowledge proof,oblivious transfer and private comparison protocol in implementation SMC are studied deeply.Finally,some research directions in this area are also proposed.
Secure multiparty computation (SMC) has gained tremendous importance with the growth of the Internet and e-commerce, where mutually untrusted parties need to jointly compute a function of their private inputs. However, SMC protocols usually have very high computational complexities, rendering them practically unusable. In this paper, we tackle the problem of comparing two input values in a secure distributed fashion. We propose efficient secure comparison protocols for both the homomorphic encryption and secret sharing schemes. We also give experimental results to show their practical relevance.
Secure multiparty computation (SMC) allows a set of parties to jointly compute a function on private inputs such that, they learn only the output of the function, and the correctness of the output is guaranteed even when a subset of the parties is controlled by an adversary. SMC allows data to be kept in an uncompromisable form and still be useful, and it also gives new meaning to data ownership, allowing data to be shared in a useful way while retaining its privacy. Thus, applications of SMC hold promise for addressing some of the security issues information-driven societies struggle with. In this thesis, we implement two SMC protocols. Our primary objective is to gain a solid understanding of the basic concepts related to SMC. We present a brief survey of the field, with focus on SMC based on secret sharing. In addition to the protocol im- plementations, we implement circuit randomization, a common technique for efficiency improvement. The implemented protocols are run on a simulator to securely evaluate some simple arithmetic functions, and the round complexities of the implemented protocols are compared. Finally, we attempt to extend the implementation to support more general computations.
As a fundamental part of cryptography, secure multiparty computation (SMC) is a building block of various cryptographic protocols, and it is also a hot topic in the international cryptographic community.In recent years, many SMC problems, such as secret information comparison, secret set problems and secure multiparty computational geometry, have been widely studied.As many practical problems need to be described by rational numbers, it is both theoretically and practically important to study the SMC problems in the rational number field.However, most of the existing researches focus on integers and the studied data are mainly one-dimensional data.There are few researches on secure multiparty computation of multi-dimensional data in the rational number field, but they can’t be generalized.Based on the fractional representation of rational numbers, the new encoding schemes about rational numbers and rational number vectors were proposed, which could encode multi-dimensional data in the rational number field and provided new solutions for other SMC problems in the rational number field.Based on the encoding scheme and one-way hash function, some protocols were designed for equality problems and set problems in the rational number field.These protocols used basic arithmetic operation and hash operation to guarantee efficiency than existing related protocols.And these protocols didn’t limit the range of research data and they were more widely applicable.It proves that these protocols are secure in the semi-honest model using simulation paradigm, and demonstrates the efficiency and the applicability of these protocols by theoretical analysis and experiment.A practical example was also given to illustrate that approaches are more versatile, and they could also be directly used to solve some secure multiparty computational geometry problems in the rational number field.
In this paper, we show how our secured multiparty computation (SMC) protocols protect the data of an organization during the war from the cyberspace war when a large number of defense units interact with one another, while hiding the identity and computations done by them. SMC is a problem of information security when large organizations interact with one another for huge data sharing and data exchange. It is quite possible that during sharing and exchange, the private data also get hacked. In order to protect and secure the private data, the protocols of SMC need to be deployed in the large computer networks on which the organizations work. The protocols work at the micro-level in terms of cryptography with which the data are encrypted and then shared, while allowing the keys to be used for sharable data while also keeping the keys untouched for private data. At the macro level, multilevel architectures are used for different types of security to be achieved. The computation part of the secured multiparty computation is based on the algorithmic complexity theory. The algorithms realize the protocols in such a way that it is tedious to break (decrypt) the keys to hack the private data.
The problem of secure multi-party computation(SMPC) is one of the most fundamental problems in information security. First, we introduce the basic concept of SMPC and four SMPC basic agreement: key distribution,oblivious transfer, bit commitment and zero knowledge proof. Secondly, we separately illustrate commitment schemes commitment transfer protocol, commitment sharing protocol and commitment multiplication protocol. Finally, we present unconditionally secure multi-party computation with a passive adversary, an active adversary, general adversary structures.
Secure Multiparty Computation (SMC) allows multiple individuals, each having their own private data, to perform a computation task without unveiling their data to others. SMC has a wide range of real-life use cases, including machine learning, vehicular networks, banking, defense, and healthcare. However, classical SMC implementations face significant challenges related to both security and efficiency. Classical SMC protocols rely on public-key cryptographic methods, which introduce substantial computational and communication costs. Additionally, these protocols are vulnerable to quantum computer attacks that have the potential to break certain types of cryptographic ciphers, notably those used in RSA. In this thesis, we tackle these challenges by enhancing the security and efficiency of SMC implementations through quantum communication and quantum computing. Quantum communication contributes to enhancing security, while quantum computing, by leveraging quantum processors, offers the potential to significantly improve efficiency. In the quantum communication approach, we propose a Quantum SMC (QSMC) framework that leverages three advanced quantum communication technologies - Quantum Random Number Generation (QRNG), Quantum Key Distribution (QKD), and Quantum Oblivious Key Distribution (QOKD)—in conjunction with a Key Management System (KMS) and the Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol. Exploiting the proposed framework, we implement two essential SMC use cases: Safe Route Departure (SRD) and Drug Solubility Prediction (DSP), applied to vehicular networks and drug discovery, respectively. The SRD facilitates secure vehicular communication, allowing vehicles to safely change lanes and exit routes without compromising sensitive information. We achieved a 97% improvement in efficiency by significantly reducing communication costs, while incurring a moderate 42% increase in computational cost. The DSP, on the other hand, empowers pharmaceutical companies to collaboratively train a Graph Convolutional Network (GCN) to predict the solubility of drug molecules while fully safeguarding their private datasets. During training, the loss function across all parties averaged 0.0046, while the Mean Squared Error (MSE) on the test set is 1.2, indicating effective model learning. Afterwards, exploiting the quantum computing approach, we propose two novel SMC protocols for Boolean function computation resorting to the Measurement Based Quantum Computing (MBQC) approach and single qubits. We implement the proposed schemes on the IBM Qiskit platform and validate their feasibility.