Abstract Secure multiparty computation (SMC) provides collaboration among multiple parties, ensuring the confidentiality of their private information. However, classical SMC implementations encounter significant security and efficiency challenges. Resorting to the entangled Greenberger–Horne–Zeilinger (GHZ) state, we propose a quantum-based two-party protocol to compute binary Boolean functions, with the help of a third party. We exploit a technique in which a random Z-phase rotation on the GHZ state is performed to achieve higher security. The security and complexity analyses demonstrate the feasibility and improved security of our scheme compared to other SMC Boolean function computation methods. Additionally, we implemented the proposed protocol on the IBM QisKit and found consistent outcomes that validate the protocol’s correctness.
Abstract Secure multiparty computation (SMC) provides collaboration among multiple parties, ensuring the confidentiality of their private information. However, classical SMC implementations encounter significant security and efficiency challenges. Resorting to the entangled Greenberger–Horne–Zeilinger (GHZ) state, we propose a quantum-based two-party protocol to compute binary Boolean functions, with the help of a third party. We exploit a technique in which a random Z-phase rotation on the GHZ state is performed to achieve higher security. The security and complexity analyses demonstrate the feasibility and improved security of our scheme compared to other SMC Boolean function computation methods. Additionally, we implemented the proposed protocol on the IBM QisKit and found consistent outcomes that validate the protocol’s correctness.
Secure multi-party computation (SMC) is an important privacy-preserving technology with broad applications in fields such as data mining and cloud computing. As a significant branch of SMC, private set computation (PSC) allows the computation of the intersection or union of private datasets from two or more participants while protecting their data privacy. Quantum private set computation (QPSC) is a new direction that combines PSC with quantum mechanics and has recently garnered widespread attention. Most existing QPSC protocols rely on quantum measurements performed by participants or third parties, but the measurement devices used in practice may be vulnerable to side-channel attacks. To address this issue, this paper proposes a measurement-device-independent QPSC protocol. In this protocol, participants can utilize post-selected Greenberger-Horne-Zeilinger (GHZ) state entanglement to compute the private intersection and union of their datasets securely. Analysis shows that this protocol is correct and can resist potential external and internal attacks. Finally, the protocol's feasibility is verified through simulation on the HIQ quantum computing cloud platform using the MindSpore Quantum framework.
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.
We introduce a scheme for secure multiparty computation utilizing the quantum correlations of entangled states. First we present a scheme for two-party computation, exploiting the correlations of a Greenberger-Horne-Zeilinger state to provide, with the help of a third party, a near-private computation scheme. We then present a variation of this scheme which is passively secure with threshold t=2, in other words, remaining secure when pairs of players conspire together provided they faithfully follow the protocol. Furthermore, we show that the passively secure variant can be modified to be secure when cheating parties are allowed to deviate from the protocol. We show that this can be generalized to computations of n-party polynomials of degree 2 with a threshold of $n\ensuremath{-}1$. The threshold achieved is significantly higher than the best known classical threshold, which satisfies the bound t<n/2. Our schemes, each complying with a different definition of security, shed light on which physical assumptions are necessary in order to achieve quantum secure multiparty computation.
Secure multi-party extremum, as a significant offshoot of secure multi-party computation, has extensive applications in various domains, including healthcare, financial transactions, market analysis, sports events, etc. Nevertheless, most existing secure multi-party extremum protocols rely on computational hard problems and are thus vulnerable to quantum algorithms. This paper presents a quantum secure multi-party extremum protocol that is built upon the correlations of Greenberger–Horne–Zeilinger (GHZ) states. Within this protocol, multiple participants, with the aid of a semi-honest third party, can obtain the maximum and minimum values of their secret inputs. GHZ states act as the information carriers and are transmitted among the participants and the third party. Their unique correlations ensure the secure transmission of quantum particles. The analysis demonstrates that the proposed protocol is capable of not only warding off common external attacks but also resisting internal attacks launched by dishonest participants and the semi-honest third party. Moreover, the protocol boasts correctness and high scalability.
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.
Secure two-party scalar product (S2SP) is a promising research area within secure multiparty computation (SMC), which can solve a range of SMC problems, such as intrusion detection, data analysis, and geometric computations. However, existing quantum S2SP protocols are not efficient enough, and the complexity is usually close to exponential level. In this paper, a novel secure two-party quantum scalar product (S2QSP) protocol based on Fourier entangled states is proposed to achieve higher efficiency. Firstly, the definition of unconditional security under malicious models is given. And then, an honesty verification method called Entanglement Bondage is proposed, which is used in conjunction with the modular summation gate to resist malicious attacks. The property of Fourierentangled states is used to calculate the scalar product with polynomial complexity. The unconditional security of our protocol is proved, which guarantees the privacy of all parties. In addition, we design a privacy-preserving quantum matrix multiplication protocol based on S2QSP protocol. By transforming matrix multiplication into a series of scalar product processes, the product of two private matrices is calculated without revealing any privacy. Finally, we show our protocol’s feasibility in IBM Qiskit simulator.