Efficient quantum homomorphic encryption scheme with flexible evaluators and its simulation

Liu, Jiang; Li, Qin; Quan, Junyu; Wang, Can; Shi, Jinjing; Situ, Haozhen

doi:10.1007/s10623-021-00993-2

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…Unless quantum technology is integrated into blockchain technology, the components of classical blockchain are exposed to quantum attacks [5], resulting in security risks to the internal technologies and external performance of the blockchain. Currently, we have already seen the development of information-theoretically secure quantum protocols, such as quantum homomorphic encryption protocols [15,16], quantum image encryption [17,18], and quantum digital signature [19,20], while several attempts have been made to incorporate post-quantum cryptography [21,22]. These protocols bring benefits to the internal technologies and external performance of blockchain, achieving privacy, security, effectiveness, and profitability under quantum attacks.…”

Section: Related Books and Surveysmentioning

confidence: 99%

Building Resilient Web 3.0 with Quantum Information Technologies and Blockchain: An Ambilateral View

Ren¹,

Xu²,

Niyato³

et al. 2023

Preprint

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Web 3.0 pursues the establishment of decentralized ecosystems based on blockchain technologies to drive the digital transformation of physical commerce and governance. Through consensus algorithms and smart contracts in blockchain, which are based on cryptography technologies, digital identity, digital asset management, decentralized autonomous organization, and decentralized finance are realized for secure and transparent digital economy services in Web 3.0 for promoting the integration of digital and physical economies. With the rapid realization of quantum devices, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with the advantages of quantum computing and communication. Therefore, this survey provides a comprehensive overview of blockchain-based Web 3.0 and its quantum and post-quantum enhancement from the ambilateral perspective. On the one hand, some post-quantum migration methods, and anti-quantum signatures offer potential ways to achieve unforgeable security under quantum attack for the internal technologies of blockchain. On the other hand, some quantum/post-quantum encryption and verification algorithms improve the external performance of the blockchain, enabling a decentralized, valuable, secure blockchain system. Finally, we discuss the future directions toward developing a provable secure decentralized digital ecosystem.CCS Concepts: • Security and privacy → Cryptography; • Theory of computation → Models of computation .

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Section: Related Books and Surveysmentioning

confidence: 99%

Building Resilient Web 3.0 with Quantum Information Technologies and Blockchain: An Ambilateral View

Ren¹,

Xu²,

Niyato³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The privacy protection of quantum machine learning is an emergent field and there is a lot of research about it. Most of the existing literature protects privacy by means of quantum homomorphic encryption [15][16][17][18][19][20][21][22], quantum differential privacy [23,24] and quantum secure multi-party computing [25][26][27]. Each method achieves privacy protection in different ways and presents different levels of security and availability.…”

Section: Introductionmentioning

confidence: 99%

Quantum neural network with privacy protection of input data and training parameters

Fang,

Chang

2024

Phys. Scr.

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It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for R z , R x and R y gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O(4n).

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“…Subsequently, Fisher et al [15] proposed a key update algorithm for quantum operations on ciphertext data, and Liang et al [16] proposed the concept of quantum complete homomorphic encryption. Since then, QHE has been widely used in quantum encryption computing [17][18][19][20]. In recent years, QHE has been gradually applied to quantum machine learning.…”

Section: Introductionmentioning

confidence: 99%

Privacy protection of quantum BP neural network based on game theory

Lin,

Chang,

Huang

et al. 2023

Phys. Scr.

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How to ensure privacy security and improve computing efficiency is a research hotspot in the field of machine learning. Among them, how to balance the interests of users, cloud servers and attackers on the premise of ensuring user privacy is a difficult problem in the field of machine learning privacy protection. The development of quantum computing breaks through the computational bottleneck of classical machine learning and has derived the research direction of quantum machine learning. At present, hybrid quantum classical machine learning in NISQ era has become a research hotspot, but researchers rarely pay attention to the privacy protection in quantum machine learning. Therefore, this paper is the first to apply game theory to the privacy protection in quantum machine learning and proposes the privacy game model of user - server - attacker in Hybrid Classical Quantum BP Neural Network (HCQBPNN). Different from previous studies, this paper sets game strategies based on users' privacy requirements in practical applications, and aims to maximize the interests of attackers, cloud servers and users. The experiment proves that users can use the privacy game model proposed in this paper to get the optimal privacy combination strategy, and at the same time make the cloud server and the attacker can obtain positive income.

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Efficient quantum homomorphic encryption scheme with flexible evaluators and its simulation

Cited by 16 publications

References 25 publications

Building Resilient Web 3.0 with Quantum Information Technologies and Blockchain: An Ambilateral View

Building Resilient Web 3.0 with Quantum Information Technologies and Blockchain: An Ambilateral View

Quantum neural network with privacy protection of input data and training parameters

Privacy protection of quantum BP neural network based on game theory

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