Publicação
Secure multiparty computation based on quantum technologies
| Resumo: | 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. |
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| Autores principais: | Rahmani, Zeinab |
| Assunto: | Quantum secure multiparty computation Quantum cryptography Quantum random number generation Quantum key distribution Quantum oblivious key distribution Quantum oblivious transfer Measurement based quantum computing Vehicular networks Drug discovery |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | 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. |
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