Secure computation of scalar product is of considerable importance due to its central role in many practical computation scenarios with privacy and security requirements. This paper includes new results about the secure two-party scalar product. Specifically, a perfectly secure and universally composable two-party split scalar product (SSP) protocol is proposed in the preprocessing model. In addition to full security, the proposed SSP protocol enjoys the advantage of optimal efficiency. To show the optimality of this SSP protocol, information theoretic lower bounds on the amount of communicated data in a secure two-party computation in the preprocessing model are derived. These bounds are not limited to SSP functionality but apply to a large class of two-party functionalities. A part of this paper is devoted to applications of the proposed SSP protocol in secure cloud computing. Specifically, based on this protocol, a cloud-assisted privacy-preserving profile-matching scheme and a secure remote health monitoring scheme are proposed. Both of the solutions are highly efficient and significantly improve the previous work.
Secure computation of scalar product is of considerable importance due to its central role in many practical computation scenarios with privacy and security requirements. This paper includes new results about the secure two-party scalar product. Specifically, a perfectly secure and universally composable two-party split scalar product (SSP) protocol is proposed in the preprocessing model. In addition to full security, the proposed SSP protocol enjoys the advantage of optimal efficiency. To show the optimality of this SSP protocol, information theoretic lower bounds on the amount of communicated data in a secure two-party computation in the preprocessing model are derived. These bounds are not limited to SSP functionality but apply to a large class of two-party functionalities. A part of this paper is devoted to applications of the proposed SSP protocol in secure cloud computing. Specifically, based on this protocol, a cloud-assisted privacy-preserving profile-matching scheme and a secure remote health monitoring scheme are proposed. Both of the solutions are highly efficient and significantly improve the previous work.
Abstract With the rapid development of distributed collaborative computing, privacy leakage risks in multi-source data joint analysis are increasing, and classical secure scalar product (SSP) protocols are vulnerable to quantum attacks. Existing quantum secure scalar product schemes mostly rely on complex quantum resources or third parties, only support two-party computation, and have poor scalability. To address these issues, this paper proposes a secure multi-party quantum scalar product protocol based on single-photon resources without a third party. The protocol adopts a two-layer architectural design, integrating pairwise secure product operation and privacy-preserving aggregation mechanism. It realizes result blinding via data splitting and reconstruction to fully guarantee data confidentiality. By adopting ring interaction structure and lightweight secure computing modes, it completes collaborative computation of multi-party private data and effectively prevents information leakage in interactive processes. Performance analysis shows that the protocol requires no more than single-photon and single-particle measurements, with complexity within a reasonable range, making it feasible with current quantum technology. This protocol provides an efficient and secure primitive for multi-party privacy-preserving collaborative computing in quantum-resistant scenarios.
The emergence of cloud computing provides a new computing paradigm for users -- massive and complex computing tasks can be outsourced to cloud servers. However, the privacy issues also follow. Fully homomorphic encryption shows great potential in privacy-preserving computation, yet it is not ready for practice. At present, secure multiparty computation (MPC) remains mainly approach to deal with sensitive data. In this paper, following the secret sharing based MPC paradigm, we propose a secure 2-party computation scheme, in which cloud servers can securely evaluate functions with high efficiency. We first propose the multiplicative secret sharing (MSS) based on typical additive secret sharing (ASS). Then, we design protocols to switch shared secret between MSS and ASS, based on which a series of protocols for comparison and nearly all of the elementary functions are proposed. We prove that all the proposed protocols are Universally Composable secure in the honest-but-curious model. Finally, we will show the remarkable progress of our protocols on both communication efficiency and functionality completeness.
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.
Secure scalar product protocol is an important fundamental protocol in secure multi-party computation. Serving as a basic building block for many other secure protocols, it is widely used in data mining, statistical analysis and scientific computation. Based on additive homomorphism public key cryptosystem, we develop a new secure scalar product protocol under semi-honest model with low communication complexity which is easy to implement and understanding. Furthermore, we firstly apply it to position relationship decision for privacy preserving space vectors.
Abstract Cloud computing has data leakage from all parties, security protection of private data, and existing solutions do not provide a trade-off between security and overhead. With distributed data communication due to data barriers, information interaction security and data computation security have become challenges for secure computing. Combining cloud computing with secure multiparty computation can provide a higher level of data protection while maintaining the benefits of cloud computing. In this case, data can be stored in the cloud and computed through SMPC protocols, thus protecting the privacy and security of the data. However, multiple rounds of information interaction are often required, increasing the communication overhead, and the security strength is limited by the hardness assumption. In this paper, we work to achieve an optimal setting of the number of rounds in secure multi-party computation on the cloud to achieve a sublinear communication overhead and improve the security concept. A 2-round SMPC protocol is constructed in the framework of Universally Composable (UC). A 2-round SMPC protocol is constructed that uses multilinear maps based on the Learning from Errors (LWE) assumption. The participant encodes the input and sends it via broadcast to reduce the interaction, homomorphic computational encoding information for secure access to computational data and secure the SMPC protocol through UC security. This paper extends the participants to multiple parties, reduces the communication rounds to 2, the protocol achieves sublinear communication overhead in poly polynomial time, smaller setup size to poly(k), and static security is achieved.
The problem of three-party secure computation, where a function of private data of two parties is to be computed by a third party without revealing information beyond respective inputs or outputs is considered. New and better lower bounds on the amount of communication required between the parties to guarantee zero probability of error in the computation and achieve information-theoretic security are derived. Protocols are presented and proved to be optimal in some cases by showing that they achieve the improved lower bounds.