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  • Open Access


    An Enhanced Privacy Preserving, Secure and Efficient Authentication Protocol for VANET

    Safiullah Khan1, Ali Raza2,3, Seong Oun Hwang4,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3703-3719, 2022, DOI:10.32604/cmc.2022.023476

    Abstract Vehicular ad hoc networks (VANETs) have attracted growing interest in both academia and industry because they can provide a viable solution that improves road safety and comfort for travelers on roads. However, wireless communications over open-access environments face many security and privacy issues that may affect deployment of large-scale VANETs. Researchers have proposed different protocols to address security and privacy issues in a VANET, and in this study we cryptanalyze some of the privacy preserving protocols to show that all existing protocols are vulnerable to the Sybil attack. The Sybil attack can be used by malicious actors to create fake… More >

  • Open Access


    Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

    Akashdeep Bhardwaj1, Ahmed A. Mohamed2,3,*, Manoj Kumar1, Mohammed Alshehri4, Ahed Abugabah5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1017-1032, 2022, DOI:10.32604/cmc.2022.018736

    Abstract The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated… More >

  • Open Access


    A Survey on Recent Advances in Privacy Preserving Deep Learning

    Siran Yin1,2, Leiming Yan1,2,*, Yuanmin Shi1,2, Yaoyang Hou1,2, Yunhong Zhang1,2

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 175-185, 2020, DOI:10.32604/jihpp.2020.010780

    Abstract Deep learning based on neural networks has made new progress in a wide variety of domain, however, it is lack of protection for sensitive information. The large amount of data used for training is easy to cause leakage of private information, thus the attacker can easily restore input through the representation of latent natural language. The privacy preserving deep learning aims to solve the above problems. In this paper, first, we introduce how to reduce training samples in order to reduce the amount of sensitive information, and then describe how to unbiasedly represent the data with respect to specific attributes,… More >

  • Open Access


    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

    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 technology to create secure ACOMKSVM… More >

  • Open Access


    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


    A Block Compressed Sensing for Images Selective Encryption in Cloud

    Xingting Liu1, Jianming Zhang2,*, Xudong Li2, Siwang Zhou1, Siyuan Zhou2, Hye-JinKim3

    Journal of Cyber Security, Vol.1, No.1, pp. 29-41, 2019, DOI:10.32604/jcs.2019.06013

    Abstract The theory of compressed sensing (CS) has been proposed to reduce the processing time and accelerate the scanning process. In this paper, the image recovery task is considered to outsource to the cloud server for its abundant computing and storage resources. However, the cloud server is untrusted then may pose a considerable amount of concern for potential privacy leakage. How to protect data privacy and simultaneously maintain management of the image remains challenging. Motivated by the above challenge, we propose an image encryption algorithm based on chaotic system, CS and image saliency. In our scheme, we outsource the image CS… More >

  • Open Access


    Research on Privacy Preserving Data Mining

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

    Journal of Information Hiding and Privacy Protection, Vol.1, No.2, pp. 61-68, 2019, DOI:10.32604/jihpp.2019.05943

    Abstract In recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed. 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


    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 >

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