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  • Open Access

    ARTICLE

    Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

    Bao Le Nguyen1, E. Laxmi Lydia2, Mohamed Elhoseny3, Irina V. Pustokhina4, Denis A. Pustokhin5, Mahmoud Mohamed Selim6, Gia Nhu Nguyen7, 8, K. Shankar9, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 87-107, 2020, DOI:10.32604/cmc.2020.011599 - 23 July 2020

    Abstract In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Decision Protocols Based on Quantum Oblivious Key Distribution

    Kejia Zhang1, 2, 3, 4, Chunguang Ma5, Zhiwei Sun4, 6, *, Xue Zhang2, 3, Baomin Zhou2, Yukun Wang7

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1915-1928, 2020, DOI:10.32604/cmc.2020.09836 - 30 June 2020

    Abstract Oblivious key transfer (OKT) is a fundamental problem in the field of secure multi-party computation. It makes the provider send a secret key sequence to the user obliviously, i.e., the user may only get almost one bit key in the sequence which is unknown to the provider. Recently, a number of works have sought to establish the corresponding quantum oblivious key transfer model and rename it as quantum oblivious key distribution (QOKD) from the well-known expression of quantum key distribution (QKD). In this paper, a new QOKD model is firstly proposed for the provider and More >

  • Open Access

    ARTICLE

    Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography

    Yun Tan1, Jiaohua Qin1, *, Hao Tang2, Xuyu Xiang1, Ling Tan2, Neal N. Xiong3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1797-1817, 2020, DOI:10.32604/cmc.2020.010802 - 30 June 2020

    Abstract With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with 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 More >

  • Open Access

    ARTICLE

    Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

    Wenxi Han1, 2, Mingzhi Cheng3, *, Min Lei1, 2, Hanwen Xu2, Yu Yang1, 2, Lei Qian4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1025-1038, 2020, DOI:10.32604/cmc.2020.09815 - 10 June 2020

    Abstract In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road… More >

  • Open Access

    ARTICLE

    Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform

    Jiaye Pan1, Yi Zhuang1, *, Xinwen Hu1, 2, Wenbing Zhao3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 607-622, 2020, DOI:10.32604/cmc.2020.09853 - 20 May 2020

    Abstract Nowadays cloud architecture is widely applied on the internet. New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms. In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis, high performance overhead and lack of applicability, this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components, which is used to detect the possible privacy leakage of applications installed on cloud platforms. It can be used online in cloud platform environments for fine-grained automated… More >

  • Open Access

    ARTICLE

    δ-Calculus: A New Approach to Quantifying Location Privacy☆

    Lihua Yin1, Ran Li1, 2, *, Jingquan Ding3, 4, 5, *, Xiao Li3, 4, 5, Yunchuan Guo2, Huibing Zhang6, Ang Li7

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1323-1342, 2020, DOI:10.32604/cmc.2020.09667 - 30 April 2020

    Abstract With the rapid development of mobile wireless Internet and high-precision localization devices, location-based services (LBS) bring more convenience for people over recent years. In LBS, if the original location data are directly provided, serious privacy problems raise. As a response to these problems, a large number of location-privacy protection mechanisms (LPPMs) (including formal LPPMs, FLPPMs, etc.) and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy leakage. However, existing schemes independently consider FLPPMs and evaluation metrics, without synergizing them into a unifying framework. In this paper, a… More >

  • Open Access

    ARTICLE

    OTT Messages Modeling and Classification Based on Recurrent Neural Networks

    Guangyong Yang1, Jianqiu Zeng1, Mengke Yang2, *, Yifei Wei3, Xiangqing Wang3, Zulfiqar Hussain Pathan4

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 769-785, 2020, DOI:10.32604/cmc.2020.07528 - 01 May 2020

    Abstract A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with More >

  • Open Access

    ARTICLE

    A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

    Shuyu Li1, Guozheng Zhang1, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 223-241, 2020, DOI:10.32604/cmc.2020.07499 - 30 March 2020

    Abstract With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different… More >

  • Open Access

    ARTICLE

    Access Control Policy Based on Friend Circle

    Qin Liu1, Tinghuai Ma1, 2, *, Fan Xing1, Yuan Tian3, Abdullah Al-Dhelaan3, Mohammed Al-Dhelaan3

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1143-1159, 2020, DOI:10.32604/cmc.2020.04949

    Abstract Nowadays, the scale of the user’s personal social network (personal network, a network of the user and their friends, where the user we call “center user”) is becoming larger and more complex. It is difficult to find a suitable way to manage them automatically. In order to solve this problem, we propose an access control model for social network to protect the privacy of the central users, which achieves the access control accurately and automatically. Based on the hybrid friend circle detection algorithm, we consider the aspects of direct judgment, indirect trust judgment and malicious More >

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