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  • Open Access

    ARTICLE

    Improved Homomorphic Encryption with Optimal Key Generation Technique for VANETs

    G. Tamilarasi1,*, K. Rajiv Gandhi2, V. Palanisamy1

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1273-1288, 2022, DOI:10.32604/iasc.2022.024687

    Abstract In recent years, vehicle ad hoc networks (VANETs) have garnered considerable interest in the field of intelligent transportation systems (ITS) due to the added safety and preventive measures for drivers and passengers. Regardless of the benefits provided by VANET, it confronts various challenges, most notably in terms of user/message security and privacy. Due to the decentralised nature of VANET and its changeable topologies, it is difficult to detect rogue or malfunctioning nodes or users. Using an improved grasshopper optimization algorithm (IGOA-PHE) technique in VANETs, this research develops a new privacy-preserving partly homomorphic encryption with optimal key generation. The suggested IGOA-PHE… More >

  • Open Access

    ARTICLE

    An Effective Blockchain Based Secure Searchable Encryption System

    Aitizaz Ali1, Mehedi Masud2, Ateeq ur Rehman3, Can Chen1, Mehmood4, Mohammad A. AlZain5, Jehad Ali6,*

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1183-1195, 2022, DOI:10.32604/iasc.2022.023930

    Abstract Security of Patient health records (PHR) is the most important aspect of cryptography over the Internet due to its value and importance preferably in the medical Internet of Things (IoT). Search keywords access mechanism is one of the common approaches which is used to access PHR from database, but it is susceptible to various security vulnerabilities. Although Blockchain-enabled healthcare systems provide security, but it may lead to some loopholes in the existing schemes. However, these methods primarily focused on data storage, and blockchain is used as a database. In this paper, Blockchain as a distributed database is proposed with homomorphic… More >

  • Open Access

    ARTICLE

    Road Distance Computation Using Homomorphic Encryption in Road Networks

    Haining Yu1, Lailai Yin1,*, Hongli Zhang1, Dongyang Zhan1,2, 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 >

  • Open Access

    ARTICLE

    Blockchain-Based Decision Tree Classification in Distributed Networks

    Jianping Yu1,2,3, Zhuqing Qiao1, Wensheng Tang1,2,3,*, 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 >

  • Open Access

    ARTICLE

    Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

    Muhammad Tayyab1,*, 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 >

  • Open Access

    ARTICLE

    A Novel Privacy‐Preserving Multi‐Attribute Reverse Auction Scheme with Bidder Anonymity Using Multi‐Server Homomorphic Computation

    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 >

  • Open Access

    ARTICLE

    Privacy-Preserving Genetic Algorithm Outsourcing in Cloud Computing

    Leqi Jiang1, 2, Zhangjie Fu1, 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 >

  • Open Access

    ARTICLE

    A Privacy Preserving Deep Linear Regression Scheme Based on Homomorphic Encryption

    Danping Dong1, *, 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 >

  • Open Access

    ARTICLE

    Achieving Privacy-Preserving Iris Identification Via El Gamal

    Yong Ding1, Lei Tian1, Bo Han2, Huiyong Wang2,*, 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 >

  • Open Access

    ARTICLE

    A Cryptograph Domain Image Retrieval Method Based on Paillier Homomorphic Block Encryption

    Wenjia Xu1, Shijun Xiang1,*, 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 >

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