Home / Journals / JIHPP / Vol.3, No.4, 2021
  • Open Access

    ARTICLE

    Verifiable Privacy-Preserving Neural Network on Encrypted Data

    Yichuan Liu1, Chungen Xu1,*, Lei Xu1, Lin Mei1, Xing Zhang2, Cong Zuo3
    Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 151-164, 2021, DOI:10.32604/jihpp.2021.026944
    Abstract The widespread acceptance of machine learning, particularly of neural networks leads to great success in many areas, such as recommender systems, medical predictions, and recognition. It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers. However, it must be taken into consideration that the data from clients may be exposed to cloud servers. Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes. But these architectures are based on honest but curious cloud servers, which are unable to tell… More >

  • Open Access

    ARTICLE

    A Survey on Binary Code Vulnerability Mining Technology

    Pengzhi Xu1,2, Zetian Mai1,2, Yuhao Lin1, Zhen Guo1,2,*, Victor S. Sheng3
    Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 165-179, 2021, DOI:10.32604/jihpp.2021.027280
    Abstract With the increase of software complexity, the security threats faced by the software are also increasing day by day. So people pay more and more attention to the mining of software vulnerabilities. Although source code has rich semantics and strong comprehensibility, source code vulnerability mining has been widely used and has achieved significant development. However, due to the protection of commercial interests and intellectual property rights, it is difficult to obtain source code. Therefore, the research on the vulnerability mining technology of binary code has strong practical value. Based on the investigation of related technologies, this article firstly introduces the… More >

  • Open Access

    ARTICLE

    An Explanatory Strategy for Reducing the Risk of Privacy Leaks

    Mingting Liu1, Xiaozhang Liu1,*, Anli Yan1, Xiulai Li1,2, Gengquan Xie1, Xin Tang3
    Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 181-192, 2021, DOI:10.32604/jihpp.2021.027385
    Abstract As machine learning moves into high-risk and sensitive applications such as medical care, autonomous driving, and financial planning, how to interpret the predictions of the black-box model becomes the key to whether people can trust machine learning decisions. Interpretability relies on providing users with additional information or explanations to improve model transparency and help users understand model decisions. However, these information inevitably leads to the dataset or model into the risk of privacy leaks. We propose a strategy to reduce model privacy leakage for instance interpretability techniques. The following is the specific operation process. Firstly, the user inputs data into… More >

  • Open Access

    ARTICLE

    A Performance Study of Membership Inference Attacks on Different Machine Learning Algorithms

    Jumana Alsubhi1, Abdulrahman Gharawi1, Mohammad Alahmadi2,*
    Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 193-200, 2021, DOI:10.32604/jihpp.2021.027871
    Abstract Nowadays, machine learning (ML) algorithms cannot succeed without the availability of an enormous amount of training data. The data could contain sensitive information, which needs to be protected. Membership inference attacks attempt to find out whether a target data point is used to train a certain ML model, which results in security and privacy implications. The leakage of membership information can vary from one machine-learning algorithm to another. In this paper, we conduct an empirical study to explore the performance of membership inference attacks against three different machine learning algorithms, namely, K-nearest neighbors, random forest, support vector machine, and logistic… More >

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