Haining Yu¹, Lailai Yin^1,*, Hongli Zhang¹, Dongyang Zhan^1,2, Jiaxing Qu³, Guangyao Zhang⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3445-3458, 2021, DOI:10.32604/cmc.2021.019462

Abstract Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services. The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation. To alleviate privacy concerns about location privacy leaks during road distance computation, it is desirable to have a secure and efficient road distance computation approach. In this paper, we propose two secure road distance computation approaches, which can compute road distance over encrypted data efficiently. An approximate road distance computation approach is designed by using Partially Homomorphic… More >

Jianping Yu^1,2,3, Zhuqing Qiao¹, Wensheng Tang^1,2,3,*, Danni Wang¹, Xiaojun Cao⁴

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 713-728, 2021, DOI:10.32604/iasc.2021.017154

Abstract In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model’s accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient Blockchain-based ID3 Decision Tree Classification… 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 >

Wenbo Shi¹, Jiaqi Wang², Jinxiu Zhu³, YuPeng Wang⁴, Dongmin Choi⁵

Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 171-181, 2019, DOI:10.31209/2018.100000053

Abstract With the further development of Internet, the decision-making ability of the smart service is getting stronger and stronger, and the electronic auction is paid attention to as one of the ways of decision system. In this paper, a secure multi-attribute reverse auction protocol without the trusted third party is proposed. It uses the Paillier public key cryptosystem with homomorphism and combines with oblivious transfer and anonymization techniques. A single auction server easily collides with a bidder, in order to solve this problem, a single auction server is replaced with multiple auction servers. The proposed scheme uses multiple auction servers to… More >

Leqi Jiang^{1, 2}, Zhangjie Fu^{1, 2, *}

Journal of Cyber Security, Vol.2, No.1, pp. 49-61, 2020, DOI:10.32604/jcs.2020.09308

Abstract Genetic Algorithm (GA) has been widely used to solve various optimization problems. As the solving process of GA requires large storage and computing resources, it is well motivated to outsource the solving process of GA to the cloud server. However, the algorithm user would never want his data to be disclosed to cloud server. Thus, it is necessary for the user to encrypt the data before transmitting them to the server. But the user will encounter a new problem. The arithmetic operations we are familiar with cannot work directly in the ciphertext domain. In this paper, a privacy-preserving outsourced genetic… More >

Danping Dong^{1, *}, Yue Wu¹, Lizhi Xiong¹, Zhihua Xia¹

Journal on Big Data, Vol.1, No.3, pp. 145-150, 2019, DOI:10.32604/jbd.2019.08706

Abstract This paper proposes a strategy for machine learning in the ciphertext domain. The data to be trained in the linear regression equation is encrypted by SHE homomorphic encryption, and then trained in the ciphertext domain. At the same time, it is guaranteed that the error of the training results between the ciphertext domain and the plaintext domain is in a controllable range. After the training, the ciphertext can be decrypted and restored to the original plaintext training data. More >

Yong Ding¹, Lei Tian¹, Bo Han², Huiyong Wang^2,*, Yujue Wang¹, James Xi Zheng³

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 727-738, 2019, DOI:10.32604/cmc.2019.06220

Abstract Currently, many biometric systems maintain the user’s biometrics and templates in plaintext format, which brings great privacy risk to uses’ biometric information. Biometrics are unique and almost unchangeable, which means it is a great concern for users on whether their biometric information would be leaked. To address this issue, this paper proposes a confidential comparison algorithm for iris feature vectors with masks, and develops a privacy-preserving iris verification scheme based on the El Gamal encryption scheme. In our scheme, the multiplicative homomorphism of encrypted features is used to compare of iris features and their mask information. Also, this paper improves… More >

Wenjia Xu¹, Shijun Xiang^1,*, Vasily Sachnev²

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 285-295, 2018, DOI:10.3970/cmc.2018.01719

Abstract With the rapid development of information network, the computing resources and storage capacity of ordinary users cannot meet their needs of data processing. The emergence of cloud computing solves this problem but brings data security problems. How to manage and retrieve ciphertext data effectively becomes a challenging problem. To these problems, a new image retrieval method in ciphertext domain by block image encrypting based on Paillier homomophic cryptosystem is proposed in this paper. This can be described as follows: According to the Paillier encryption technology, the image owner encrypts the original image in blocks, obtains the image in ciphertext domain,… More >