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Quantum-secure protocols for privacy preserving location based services

Sushmita Sarkar, Tapaswini Mohanty, Vikas Srivastava +4 more

Optical Switching and NetworkingJun 1, 20250 citations

Privacy-preserving point-inclusion protocol for an arbitrary area based on phase-encoded quantum private query

Runhua Shi, Yi Mu, Hong Zhong +2 more

Quantum Information ProcessingDec 10, 201612 citations

Quantum Privacy-preserving Two-party Circle Intersection Protocol Based on Phase-encoded Query

Zixian Li, Qi Yang, Feng Bao +1 more

arXiv:2309.17293International Journal of Theoretical PhysicsJun 28, 20236 citations

Quantum-Enhanced Matching Mechanism for Secure and Efficient Power IoT Data Trading

Can Wang, Run-Hua SHI, Jiang-Yuan Lian +2 more

The exponential growth of power IoT data has created immense potential for intelligent energy management, but it also presents critical challenges in achieving secure and efficient data trading. In particular, emerging data trading scenarios demand support for range nearest neighbor matching, which current schemes fail to address. This paper proposes a novel quantum trading matching scheme tailored for power data markets, which, for the first time, supports range nearest neighbor matching while balancing accuracy, efficiency, and privacy. To improve matching efficiency, we design a quantum private query (QPQ) mechanism based on a bidirectional sliding window (BSW), which replaces traditional linear search with dynamic range expansion. Furthermore, to ensure strong privacy and security in real-world scenarios, we develop a two-layer QPQ framework that performs data feature matching and identity retrieval separately, supported by a customized key distribution strategy. Our solution resists quantum attacks and significantly reduces computational overhead. At the same time, by utilizing non-ideal photon sources, it offers a practical and privacy-preserving solution for large-scale power data trading and matching.The exponential growth of power IoT data has created immense potential for intelligent energy management, but it also presents critical challenges in achieving secure and efficient data trading. In particular, emerging data trading scenarios demand support for range nearest neighbor matching, which current schemes fail to address. This paper proposes a novel quantum trading matching scheme tailored for power data markets, which, for the first time, supports range nearest neighbor matching while balancing accuracy, efficiency, and privacy. To improve matching efficiency, we design a quantum private query (QPQ) mechanism based on a bidirectional sliding window (BSW), which replaces traditional linear search with dynamic range expansion. Furthermore, to ensure strong privacy and security in real-world scenarios, we develop a two-layer QPQ framework that performs data feature matching and identity retrieval separately, supported by a customized key distribution strategy. Our solution resists quantum attacks and significantly reduces computational overhead. At the same time, by utilizing non-ideal photon sources, it offers a practical and privacy-preserving solution for large-scale power data trading and matching.

IEEE Transactions on Network and Service ManagementJan 1, 20260 citations

Enhancing Privacy with Quantum Networks

Paulo Mateus, Nikola Paunković, João Rodrigues +1 more

Lecture notes in computer scienceJan 1, 20141 citation

ESPQuery: An Enhanced Secure Scheme for Privacy-Preserving Query Based on Untrusted Devices in the Internet of Things

Dandan Li, Xiaohong Huang, Wei Huang +2 more

The development of the Internet of Things (IoT) has brought various IoT services, which facilitate and enrich human life. All these services are at risk of privacy leakage. The privacy-preserving issue of IoT query, which is a typical service, has attracted much attention. A superior candidate for solving the above issue is classical cryptographic schemes based on the computational difficulty. With the advent of quantum computation, the security of such schemes may be broken by the strong ability of some advanced quantum algorithms. How to design a secure scheme for privacy-preserving query under the threat of quantum computation is crucial. To this end, we construct a general architecture of privacy-preserving query in IoT scenarios. Besides trust or honesty-but-curious models, we present a scheme for privacy-preserving query which is also valid in the scenario of untrusted devices. We provide a detailed security analysis. The result shows our scheme achieves private preservation of both service providers and clients.The development of the Internet of Things (IoT) has brought various IoT services, which facilitate and enrich human life. All these services are at risk of privacy leakage. The privacy-preserving issue of IoT query, which is a typical service, has attracted much attention. A superior candidate for solving the above issue is classical cryptographic schemes based on the computational difficulty. With the advent of quantum computation, the security of such schemes may be broken by the strong ability of some advanced quantum algorithms. How to design a secure scheme for privacy-preserving query under the threat of quantum computation is crucial. To this end, we construct a general architecture of privacy-preserving query in IoT scenarios. Besides trust or honesty-but-curious models, we present a scheme for privacy-preserving query which is also valid in the scenario of untrusted devices. We provide a detailed security analysis. The result shows our scheme achieves private preservation of both service providers and clients.

IEEE Internet of Things JournalNov 18, 20205 citations

A Novel Quantum Solution to Privacy-Preserving Nearest Neighbor Query in Location-Based Services

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