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Search Results (12)
  • Open Access


    A Survey of Privacy Preservation for Deep Learning Applications

    Ling Zhang1,*, Lina Nie1, Leyan Yu2

    Journal of Information Hiding and Privacy Protection, Vol.4, No.2, pp. 69-78, 2022, DOI:10.32604/jihpp.2022.039284

    Abstract Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly… More >

  • Open Access


    Federated Learning Based on Data Divergence and Differential Privacy in Financial Risk Control Research

    Mao Yuxin, Wang Honglin*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879

    Abstract In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without… More >

  • Open Access


    Research on Federated Learning Data Sharing Scheme Based on Differential Privacy

    Lihong Guo*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5069-5085, 2023, DOI:10.32604/cmc.2023.034571

    Abstract To realize data sharing, and to fully use the data value, breaking the data island between institutions to realize data collaboration has become a new sharing mode. This paper proposed a distributed data security sharing scheme based on C/S communication mode, and constructed a federated learning architecture that uses differential privacy technology to protect training parameters. Clients do not need to share local data, and they only need to upload the trained model parameters to achieve data sharing. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and… More >

  • Open Access

    Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

    Zhe Sun1, Jiyuan Feng1, Lihua Yin1,*, Zixu Zhang2, Ran Li1, Yu Hu1, Chongning Na3

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1867-1886, 2022, DOI:10.32604/cmc.2022.022290

    Abstract Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be… More >

  • Open Access


    XGBoost Algorithm under Differential Privacy Protection

    Yuanmin Shi1,2, Siran Yin1,2, Ze Chen1,2, Leiming Yan1,2,*

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193

    Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data. More >

  • Open Access


    Evaluating the Risk of Disclosure and Utility in a Synthetic Dataset

    Kang-Cheng Chen1, Chia-Mu Yu2,*, Tooska Dargahi3

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 761-787, 2021, DOI:10.32604/cmc.2021.014984

    Abstract The advancement of information technology has improved the delivery of financial services by the introduction of Financial Technology (FinTech). To enhance their customer satisfaction, Fintech companies leverage artificial intelligence (AI) to collect fine-grained data about individuals, which enables them to provide more intelligent and customized services. However, although visions thereof promise to make customers’ lives easier, they also raise major security and privacy concerns for their users. Differential privacy (DP) is a common privacy-preserving data publishing technique that is proved to ensure a high level of privacy preservation. However, an important concern arises from the trade-off between the data utility… More >

  • Open Access


    A Differential Privacy Based (k-Ψ)-Anonymity Method for Trajectory Data Publishing

    Hongyu Chen1, Shuyu Li1, *, Zhaosheng Zhang1

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2665-2685, 2020, DOI:10.32604/cmc.2020.010965

    Abstract In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-Ψ)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided… More >

  • Open Access


    Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy

    Haijiang Liu1, Lianwei Cui2, Xuebin Ma1, *, Celimuge Wu3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987

    Abstract Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s… More >

  • Open Access


    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

    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 network space. Then, the dynamic… More >

  • Open Access


    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

    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 privacy budget to the group… More >

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