Jing-Wen Zhang¹, Xiu-Bo Chen^1,*, Gang Xu^2,3, Heng-Ji Li⁴, Ya-Lan Wang⁵, Li-Hua Miao⁶, Yi-Xian Yang¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2835-2848, 2022, DOI:10.32604/cmc.2022.029125

Abstract The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private data. In this paper, a novel secure multiparty quantum homomorphic encryption scheme is proposed, which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest server. Firstly, each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and key. Secondly, with the help of the almost dishonest server, the non-maximally entangled states are pre-shared between the client and… More >

Meng-Wei Zhang¹, Xiu-Bo Chen¹, Haseeb Ahmad², Gang Xu^3,4,*, Yi-Xian Yang¹

Journal of Cyber Security, Vol.4, No.1, pp. 29-39, 2022, DOI:10.32604/jcs.2022.027693

Abstract Blockchain has a profound impact on all areas of society by virtue of its immutability, decentralization and other characteristics. However, blockchain faces the problem of data privacy leakage during the application process, and the rapid development of quantum computing also brings the threat of quantum attack to blockchain. In this paper, we propose a lattice-based certificateless fully homomorphic encryption (LCFHE) algorithm based on approximate eigenvector firstly. And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme, which is composed of the private key together with the secret value… More >

Muhammad Tayyab^1,*, Mohsen Marjani¹, N. Z. Jhanjhi¹, Ibrahim Abaker Targio Hashim², Abdulwahab Ali Almazroi³, Abdulaleem Ali Almazroi⁴

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199

Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training and testing phases. The main… More >