In spatial crowdsourcing tasks, the risk of privacy leakage regarding task and workers’ locations has always been a critical issue. Although many scholars have proposed solutions based on classical computing, these methods cannot withstand potential future quantum computing attacks. This paper presents a quantum-secure protocol for selecting workers in spatial crowdsourcing, which utilizes a two-stage filtering process combining quantum multi-party geometric intersection and quantum homomorphic encryption. The protocol ensures that workers are selected without disclosing the privacy information of any participants. Compared to traditional classical protocols, our solution offers significantly higher security. The correctness analysis validates the feasibility and effectiveness of the protocol.
In spatial crowdsourcing tasks, the risk of privacy leakage regarding task and workers’ locations has always been a critical issue. Although many scholars have proposed solutions based on classical computing, these methods cannot withstand potential future quantum computing attacks. This paper presents a quantum-secure protocol for selecting workers in spatial crowdsourcing, which utilizes a two-stage filtering process combining quantum multi-party geometric intersection and quantum homomorphic encryption. The protocol ensures that workers are selected without disclosing the privacy information of any participants. Compared to traditional classical protocols, our solution offers significantly higher security. The correctness analysis validates the feasibility and effectiveness of the protocol.
Abstract With the growing demand for location privacy protection and collaborative computing in spatial crowdsourcing (SC) tasks, traditional encryption methods based on computational complexity face threats from quantum computing and efficiency bottlenecks. To address this, this paper proposes a secure multi-party quantum comparison (SMQC) protocol based on quantum homomorphic encryption. By utilizing a quantum comparator and a key update mechanism, this protocol enables secure comparison of numerical values among multiple parties while maintaining encryption. Building on this foundation, the SMQC protocol is further applied to the SC environment to construct a single-point collaborative convex hull computation protocol. Through extreme point determination and convex hull boundary verification, this protocol efficiently performs multi-party collaborative convex hull computation while protecting the location privacy of participants. Analysis indicates that the convex hull computation framework built on this protocol is reliable and secure, further expanding the application of quantum-secure solutions in SC scenarios such as single-point collaborative obstacle avoidance and collaborative spatial protection.
Abstract Secure Multi-party Geometric Computation(SMGC) is an important field in Secure Multi-party Computation(SMC). It aims to enable multiple mutually distrustful parties to collaboratively perform geometric computation tasks while protecting their respective private information. As an important branch of this field, the secure two-party polygon intersection area computation focuses on enabling two untrusted parties to securely compute the overlapping area between their polygons without revealing their private coordinate information. This paper introduces a semi-honest third party and leverages quantum homomorphic encryption technology to first achieve secure two-party segment intersection computation. Subsequently, by integrating a quantum protocol for point inclusion verification within arbitrary regions, it proposes an exact method for calculating the intersection area of polygons. Finally, through comprehensive analysis, it has been demonstrated that this protocol is secure and reliable, offering new insights for future regional collaborative computing in quantum environments.
Secure multi-party computation has been playing a fundamental role in terms of classical cryptography. Quantum homomorphic encryption (QHE) could compute the encrypted data without decryption. At present, most protocols use a semi-honest third party (TP) to protect participants’ secrets. We use a quantum homomorphic encryption scheme instead of TP to protect the privacy of parties. Based on quantum homomorphic encryption, a secure multi-party quantum summation scheme is proposed in which N participants can delegate a server with strong quantum computing power to assist computation. By delegating the computation and key update processes to a server and a semi-honest key center, participants encrypt their private information data using Pauli operators to get the sum. Besides, the server can design and optimize the summation lines itself, and the correct results can be obtained even if the secret information is negative. The correctness analysis showed that the participants could correctly obtain the results of the calculation. The security analysis proves the scheme is resistant to both outside attack and participant’s attack, and is secure against collusive attack by up to N-2 participants. From the theoretical point of view, our protocol can extend to other secure multi-party computing problems.
Abstract Secure multiparty computational geometry is an essential field of secure multiparty computation. It involves solving geometric problems without disclosing any private geometric information from each participating party. Privacy-preserving point-line relation determination, a pivotal branch of secure multiparty computational geometry, holds significant potential for applications in various domains, including business, engineering, and military. In this paper, leveraging the assistance of a third party, we introduce a quantum solution to privacy-preserving point-line relation determination. This approach incorporates quantum key distribution and one-time pad encryption. Under the condition that the third party does not conspire with any participant, our protocol ensures heightened security and enhanced privacy protection compared to classical methods. This superiority is attributed to the underlying principles of quantum mechanics and the utilization of one-time pad encryption. Furthermore, we extend our protocol to involve multiple third parties, employing the technology of quantum secret sharing. In scenarios where these third parties refrain from colluding with any single participant, no information is divulged to the other participant. Consequently, the protocol becomes more rational and secure when multiple third parties are involved.
Threshold private set intersection is a basic primitive of secure multiparty computation, which has many important applications in the field of privacy preservation and information security, especially in a ride sharing system, one of the most transforming and innovative technologies enabled by the Internet of Things. In this paper, we propose a new proposal for threshold private set intersection based on quantum homomorphic encryption, in which both the number of users and the designated trusted party are flexible. Furthermore, the complicated computing task can be delegated to a server for processing contributing to the speciality of quantum homomorphic encryption. More importantly, the users’ privacy is perfectly guaranteed because all the calculations are performed on the quantum encrypted data. Therefore, this proposal is more practical compared with the prior work.
With the rapid development of quantum computing technology, traditional encryption methods face severe security threats in multi-party privacy intersection protocols in federated learning. In this paper, we propose a new protocol based on post-quantum cryptography. Firstly, lattice-based homomorphic encryption and zero-knowledge proof technology are used to achieve key generation and parameter initialization against quantum attacks. Secondly, ciphertext data encoding is carried out to support homomorphic operations. Next, a zero-knowledge proof is used to verify the correctness of the ciphertext intersection calculation. Finally, the protocol is embedded into the federated learning workflow, adaptively adjusting the parameters. Experimental results show that the protocol achieves the NIST (National Institute of Standards and Technology) security level 3, and the privacy leakage rate is less than 1.2%, the communication and computational costs are controllable, and the protocol does not bring great influence to the accuracy of the federated learning model. The experimental results verify that the protocol can provide a reliable protection for the privacy of federated learning data in the quantum era.