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  • 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

    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 user with limited quantum capabilities,… 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

    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

    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

    ARTICLE

    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

    ARTICLE

    Privacy-Preserving Quantum Two-Party Geometric Intersection

    Wenjie Liu1,2,*, Yong Xu2, James C. N. Yang3, Wenbin Yu1,2, Lianhua Chi4

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1237-1250, 2019, DOI:10.32604/cmc.2019.03551

    Abstract Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph GA (GB) as the set of numbered grids SA (SB), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation OA (OB) is firstly utilized… More >

  • Open Access

    ARTICLE

    Enabling Comparable Search Over Encrypted Data for IoT with Privacy-Preserving

    Lei Xu1, Chungen Xu1,*, Zhongyi Liu1, Yunling Wang2,3, Jianfeng Wang2,3

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 675-690, 2019, DOI:10.32604/cmc.2019.05276

    Abstract With the rapid development of cloud computing and Internet of Things (IoT) technology, massive data raises and shuttles on the network every day. To ensure the confidentiality and utilization of these data, industries and companies users encrypt their data and store them in an outsourced party. However, simple adoption of encryption scheme makes the original lose its flexibility and utilization. To address these problems, the searchable encryption scheme is proposed. Different from traditional encrypted data search scheme, this paper focuses on providing a solution to search the data from one or more IoT device by comparing their underlying numerical values.… More >

  • Open Access

    ARTICLE

    A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

    Alia Alabdulkarim1, Mznah Al-Rodhaan2, Yuan Tian*,3, Abdullah Al-Dhelaan2

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 585-601, 2019, DOI:10.32604/cmc.2019.05637

    Abstract Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust. These systems are used to improve physicians’ diagnostic processes in terms of speed and accuracy. Using data-mining techniques, a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms. In this work, we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’ information and exposing them to cyber and network attacks. Solving the same problem with a… More >

  • Open Access

    ARTICLE

    A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

    Zhihua Xia1,*, Lihua Lu1, Tong Qiu1, H. J. Shim1, Xianyi Chen1, Byeungwoo Jeon2

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 27-43, 2019, DOI:10.32604/cmc.2019.02688

    Abstract Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this… More >

  • Open Access

    ARTICLE

    On the Privacy-Preserving Outsourcing Scheme of Reversible Data Hiding over Encrypted Image Data in Cloud Computing

    Lizhi Xiong1,*, Yunqing Shi2

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 523-539, 2018, DOI: 10.3970/cmc.2018.01791

    Abstract Advanced cloud computing technology provides cost saving and flexibility of services for users. With the explosion of multimedia data, more and more data owners would outsource their personal multimedia data on the cloud. In the meantime, some computationally expensive tasks are also undertaken by cloud servers. However, the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data. Recently, this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data. In this paper, two reversible data hiding schemes are proposed for encrypted… More >

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