Individuals’ privacy and legal regulations demand genomic data be handled and studied with highly secure privacy-preserving techniques. In this work, we propose a feasible Secure Multiparty Computation (SMC) system assisted with quantum cryptographic protocols that is designed to compute a phylogenetic tree from a set of private genome sequences. This system significantly improves the privacy and security of the computation thanks to three quantum cryptographic protocols that provide enhanced security against quantum computer attacks. This system adapts several distance-based methods (Unweighted Pair Group Method with Arithmetic mean, Neighbour-Joining, Fitch-Margoliash) into a private setting where the sequences owned by each party are not disclosed to the other members present in the protocol. We theoretically evaluate the performance and privacy guarantees of the system through a complexity analysis and security proof and give an extensive explanation about the implementation details and cryptographic protocols. We also implement a quantum-assisted secure phylogenetic tree computation based on the Libscapi implementation of the Yao, the PHYLIP library and simulated keys of two quantum systems: Quantum Oblivious Key Distribution and Quantum Key Distribution. This demonstrates its effectiveness and practicality. We benchmark this implementation against a classical-only solution and we conclude that both approaches render similar execution times, the only difference being the time overhead taken by the oblivious key management system of the quantum-assisted approach.
Individuals’ privacy and legal regulations demand genomic data be handled and studied with highly secure privacy-preserving techniques. In this work, we propose a feasible Secure Multiparty Computation (SMC) system assisted with quantum cryptographic protocols that is designed to compute a phylogenetic tree from a set of private genome sequences. This system significantly improves the privacy and security of the computation thanks to three quantum cryptographic protocols that provide enhanced security against quantum computer attacks. This system adapts several distance-based methods (Unweighted Pair Group Method with Arithmetic mean, Neighbour-Joining, Fitch-Margoliash) into a private setting where the sequences owned by each party are not disclosed to the other members present in the protocol. We theoretically evaluate the performance and privacy guarantees of the system through a complexity analysis and security proof and give an extensive explanation about the implementation details and cryptographic protocols. We also implement a quantum-assisted secure phylogenetic tree computation based on the Libscapi implementation of the Yao, the PHYLIP library and simulated keys of two quantum systems: Quantum Oblivious Key Distribution and Quantum Key Distribution. This demonstrates its effectiveness and practicality. We benchmark this implementation against a classical-only solution and we conclude that both approaches render similar execution times, the only difference being the time overhead taken by the oblivious key management system of the quantum-assisted approach.
We analyse a Secure Multiparty Computation (SMC) system assisted with quantum communication technologies. This SMC system is designed to compute phylogenetic trees of SARS-CoV-2 genomes. It adapts a distance-based method known as the Unweighted Pair Group Method with Arithmetic mean (UPGMA) algorithm into a private setting where the sequences owned by each party are not disclosed to the other members present in the protocol. Our main contribution lies on the integration of two quantum communication systems (quantum oblivious transfer and quantum key distribution) with libScapi implementation of the Yao secure computation protocol. This system significantly improves security when compared to its classical implementation since both quantum oblivious transfer and quantum key distribution modules are robust under a quantum computer attack. This system is designed to be compatible with current quantum networks.
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 analyze the use of a Quantum Key Distribution (QKD) system to generate and distribute oblivious keys. Oblivious keys can be used to implement secure multiparty computation services. After defining oblivious keys, we show how they can be reduced to symmetric keys and used to implement secure links, and how they can also be used to implement secure computation services. Oblivious keys can also be reversed. In this context, we analyze the advantages of using quantum resources to generate oblivious keys. Particularly, we analyze a protocol to generate oblivious keys, considering both the efficiency and security of the process. We show that by using QKD, we can simultaneously obtain an efficient and quantum-resistant solution to generate and distribute oblivious keys. Finally, we discuss a field trial where a quantum secure multiparty computation service for calculating phylogenetic trees was implemented in the Quantum Madrid Network.
Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation can be realized using oblivious transfer as a primitive function. In this paper, we present an experimental implementation of a quantum-secure quantum oblivious transfer (QOT) protocol using an adapted quantum key distribution system combined with a bit commitment scheme, surpassing previous approaches only secure in the noisy storage model. We demonstrate the first practical application of the QOT protocol by solving the private set intersection, a prime example of secure multiparty computation, where two parties aim to find common elements in their datasets without revealing any other information. In our experiments, two banks can identify common suspicious accounts without disclosing any other data. This not only proves the experimental functionality of QOT, but also showcases its real-world commercial applications.
Secure multi-party computation (SMC) enables multiple parties to collaboratively perform computational tasks without revealing their private data. A specific application scenario within SMC is Private Set Intersection with Correlated Sum (PSI-CS), where two participating parties compute the sum of associated elements in their intersection while preserving the privacy of their individual sets. This problem arises in various contexts, including calculating conversion amounts between suppliers and platforms, such as advertising conversion amounts and agency business conversion amounts. The paper proposes a protocol that employs quantum single-photon technology to compute the PSI-CS, provides four technical solutions suitable for various scenarios ranging from small to large data volumes, sparse to dense datasets, and without revealing the cardinality of the intersection. Additionally, the method extends to computing the union set with correlated sums. We experimentally validate the proposed protocol, conduct correctness and security analyses, and demonstrate its practical significance and application value. By utilizing single-photon technology, quantum gate operations, and measurements become more accessible, simplifying the protocol’s implementation. Notably, our approach provides unconditional security against quantum computational attacks, representing a substantial improvement in both efficiency and practical applicability compared to classical domain protocols.
Quantum Secure Multiparty Computation (QSMC) is a technology that takes the advantage of quantum features allowing multiple parties to communicate in a secure and efficient manner while preserving their privacy. Using QSMC technology, we implement a collision warning use case in which vehicles can freely broadcast information while preserving the privacy of their confidential data. We integrate two quantum technologies namely Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution (QOKD) with the Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to implement a secure and efficient QSMC platform. This quantum approach significantly improves efficiency and security when we compare it with the classical implementation as both used quantum technologies (QKD and QOKD) are robust against quantum computer attacks.