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  • Open Access


    Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

    Pingshui Wang1,*, Zecheng Wang1, Qinjuan Ma1

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 35-42, 2019, DOI:10.32604/jihpp.2019.05942

    Abstract The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications. The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control. There is little research on the association of user privacy information, so it is not easy to design personalized privacy protection strategy, but also increase the complexity of user privacy settings. Therefore, this paper concentrates on the association of user privacy information taking big data analysis tools, so as to provide data support for personalized privacy protection strategy design. More >

  • Open Access


    A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

    Liang Bai1, Yuzhen Liu1, Xiaoliang Wang1,*, Nick Patterson2, F. Jiang2

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 1-10, 2019, DOI:10.32604/jihpp.2019.06043

    Abstract Internet of Things (IoT) is an emerging paradigm involving intelligent sensor networks that incorporates embedded technology for collecting data, communicating with external environments. Recently, cloud computing together with fog computing has become an important research area of the Internet of Things because of big data processing capabilities. It is a promising technology that utilizes cloud or fog computing / architecture to improve sensor computing, storage, and communication capabilities. However, recently it has been shown that this computing/architecture may be vulnerable to various attacks because of the openness nature of the wireless network. Therefore, it becomes more and more important to… More >

  • Open Access


    A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

    Xingxing Xiao1, Yang1,*, Rui Li2, Weiming Zhang3

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 101-115, 2019, DOI:10.32604/cmc.2019.05293

    Abstract The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients’ privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for… More >

  • Open Access


    Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

    Yuhang Wang1, Yanbin Sun1,*, Shen Su1, Zhihong Tian1, Mohan Li1, Jing Qiu1, Xianzhi Wang2

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 983-993, 2019, DOI:10.32604/cmc.2019.05547

    Abstract With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS). In DLBS, the service provider (SP) will not only send the information according to the user’s location, more significant, he also provides a service device which will be carried by the user. DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London. In this paper, we, for the first time, blow the whistle of the… More >

  • Open Access


    Personalized Privacy Protecting Model in Mobile Social Network

    Pingshui Wang1,*, Zecheng Wang1, Tao Chen1,2, Qinjuan Ma1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 533-546, 2019, DOI:10.32604/cmc.2019.05570

    Abstract With the rapid development of the new generation of information technology, the analysis of mobile social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. In this paper, we summarize the main access control model in mobile social network, analyze their contribution and point out their disadvantages. On this basis, a practical privacy policy is defined through authorization model supporting personalized privacy preferences. Experiments have been conducted on synthetic data sets. The result shows that the proposed privacy protecting model could improve the security of… More >

  • Open Access


    Developing a New Security Framework for Bluetooth Low Energy Devices

    Qiaoyang Zhang1, Zhiyao Liang1,*, Zhiping Cai2

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 457-471, 2019, DOI:10.32604/cmc.2019.03758

    Abstract Wearable devices are becoming more popular in our daily life. They are usually used to monitor health status, track fitness data, or even do medical tests, etc. Since the wearable devices can obtain a lot of personal data, their security issues are very important. Motivated by the consideration that the current pairing mechanisms of Bluetooth Low Energy (BLE) are commonly impractical or insecure for many BLE based wearable devices nowadays, we design and implement a security framework in order to protect the communication between these devices. The security framework is a supplement to the Bluetooth pairing mechanisms and is compatible… More >

  • Open Access


    Privacy-Preserving Content-Aware Search Based on Two-Level Index

    Zhangjie Fu1,*, Lili Xia1, Yuling Liu2, Zuwei Tian3

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 473-491, 2019, DOI:10.32604/cmc.2019.03785

    Abstract Nowadays, cloud computing is used more and more widely, more and more people prefer to using cloud server to store data. So, how to encrypt the data efficiently is an important problem. The search efficiency of existed search schemes decreases as the index increases. For solving this problem, we build the two-level index. Simultaneously, for improving the semantic information, the central word expansion is combined. The purpose of privacy-preserving content-aware search by using the two-level index (CKESS) is that the first matching is performed by using the extended central words, then calculate the similarity between the trapdoor and the secondary… More >

  • Open Access


    Security and Privacy Frameworks for Access Control Big Data Systems

    Paolina Centonze1,*

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 361-374, 2019, DOI:10.32604/cmc.2019.06223

    Abstract In the security and privacy fields, Access Control (AC) systems are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to analyze complex and distributed Big Data (BD) processing cluster frameworks, which are adopted to manage yottabyte of unstructured sensitive data. For instance, Big Data systems’ privacy and security restrictions are most likely to failure due to the malformed AC policy configurations. Furthermore, BD systems were initially developed toped to take care of some of the DB issues to address BD challenges and many of these dealt with… More >

  • Open Access


    Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

    Mengwei Hou1, Rong Wei1,*, Tiangang Wang1, Yu Cheng2, Buyue Qian3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 137-149, 2018, DOI: 10.3970/cmc.2018.02438

    Abstract Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic… More >

  • Open Access


    Privacy-Aware Service Subscription in People-Centric Sensing: A Combinatorial Auction Approach

    Yuanyuan Xu1,*, Shan Li2, Yixuan Zhang3

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 129-139, 2019, DOI:10.32604/cmc.2019.05691

    Abstract With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things, large amount of people-centric data can be obtained and utilized to build people-centric services. Note that the service quality is highly related to the privacy level of the data. In this paper, we investigate the problem of privacy-aware service subscription in people-centric sensing. An efficient resource allocation framework using a combinatorial auction (CA) model is provided. Specifically, the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated, and it is solved by a proposed computationally tractable… More >

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