A New Quantum Private Protocol for Set Intersection Cardinality Based on a Quantum Homomorphic Encryption Scheme for Toffoli Gate

Li, Wen; Li, Yangzhi; Wang, Zhirao; Li, Yugang

doi:10.3390/e25030516

Cited by 8 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, there are only a fewstudies about the privacy protection of delegated QML. Most existing QML security schemes [23][24][25][26] are based on existing or slightly improved technologies of the above two types, thus the same shortcomings remain. Recently, a new QHE scheme [27] for fully classical clients has been proposed, but it only applies to the privacy protection of a specific type of QNN [12].…”

Section: Introductionmentioning

confidence: 99%

Delegated quantum neural networks for encrypted data

Sun,

Chang,

Wang

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

Quantum machine learning is expected to utilize the potential advantages of quantum computing to advance the efficiency of machine learning. However, with the help of quantum cloud servers, ordinary users may confront the threat of privacy leakage of input data and models when performing the training or inference of quantum neural networks (QNNs). To address this problem, we present a new framework that allows the training and inference of delegated QNNs to be performed on encrypted data to protect the privacy of users’ data and models. This framework contains two models that are alternately trained: an encryptor and a predictor. The classical client first trains the encryptor defined by a classical neural network to map plaintext input data to vastly different ciphertext data. The ciphertext data is sent to the quantum cloud server to train the predictor defined by a QNN, which can indirectly predict the labels of plaintext data. With the trained encryptor and predictor, the client can send the encrypted data to the server for prediction and obtain almost equivalent prediction results. The proposed framework is applied to three types of QNN models, each dealing with low-dimensional tabular data, image data, and one-dimensional time series data, respectively. Experimental results show that the privacy protection method based on our framework can protect data and model privacy without degrading the performance of QNNs. The framework does not require users to have quantum capabilities and is suitable for protecting data and model privacy for various QNN models.

show abstract

Section: Introductionmentioning

confidence: 99%

Delegated quantum neural networks for encrypted data

Sun,

Chang,

Wang

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

A New Protocol for Semi‐quantum Private Set of Intersection and Union Mixed Cardinality for Any Tripartite Based on Bell States

Chi,

Zhang,

Zhang

et al. 2024

Adv Quantum Tech

View full text Add to dashboard Cite

The cardinality of the private set allows multiple parties to jointly compute the cardinality of the intersection and union without revealing their respective private sets. It plays an important role in data mining and data analysis to protect privacy. However, existing studies only focus on computing the cardinality of the intersection or union of private sets. To extend its application in various scenes, a private‐set intersection and union mixed cardinality protocol are proposed for any tripartite based on Bell states for the first time. During the protocol, participants are supposed to be semi‐quantum to reduce the consumption of quantum resources and improve the realizability of the protocol. Furthermore, correctness and security analysis show that the protocol can withstand internal and external attacks. Additionally, the IBM Quantum Simulator (IBMQS) is also applied to illustrate the fundamentals of the protocol and verify the availability of the protocol. The results are expected have positive effects on the further development of secure multiparty computation.

show abstract