Chi Cui^1,2, Haiping Du², Zhijuan Jia^1,*, Yuchu He¹, Lipeng Wang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1581-1593, 2024, DOI:10.32604/cmc.2024.055557 - 15 October 2024

Abstract The rapid evolution of artificial intelligence (AI) technologies has significantly propelled the advancement of the Internet of Vehicles (IoV). With AI support, represented by machine learning technology, vehicles gain the capability to make intelligent decisions. As a distributed learning paradigm, federated learning (FL) has emerged as a preferred solution in IoV. Compared to traditional centralized machine learning, FL reduces communication overhead and improves privacy protection. Despite these benefits, FL still faces some security and privacy concerns, such as poisoning attacks and inference attacks, prompting exploration into blockchain integration to enhance its security posture. This paper… More >

Qiuyan Wang, Haibing Dong^*, Yongfei Huang, Zenglei Liu, Yundong Gou

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775 - 15 August 2024

Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… More >

Amal Al-Rasheed¹, Rahim Khan^2,3,*, Tahani Alsaed⁴, Mahwish Kundi^2,5, Mohamad Hanif Md. Saad⁶, Mahidur R. Sarker^7,8

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1305-1323, 2024, DOI:10.32604/cmc.2024.049215 - 18 July 2024

Abstract Due to the overwhelming characteristics of the Internet of Things (IoT) and its adoption in approximately every aspect of our lives, the concept of individual devices’ privacy has gained prominent attention from both customers, i.e., people, and industries as wearable devices collect sensitive information about patients (both admitted and outdoor) in smart healthcare infrastructures. In addition to privacy, outliers or noise are among the crucial issues, which are directly correlated with IoT infrastructures, as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing, i.e.,… More >

Youlin Ji, Bo Yin^*, Ke Gu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4205-4223, 2024, DOI:10.32604/cmc.2024.049086 - 26 March 2024

Abstract With its untameable and traceable properties, blockchain technology has been widely used in the field of data sharing. How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing. In this paper, we study verifiable keyword frequency (KF) queries with local differential privacy in blockchain. Both the numerical and the keyword attributes are present in data objects; the latter are sensitive and require privacy protection. However, prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries. We… More >

Teng Wang, Yao Zhang, Jiangguo Liang, Shuai Wang, Shuanggen Liu^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3891-3907, 2024, DOI:10.32604/cmc.2024.048115 - 26 March 2024

Abstract With the widespread data collection and processing, privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals. Support vector machine (SVM) is one of the most elementary learning models of machine learning. Privacy issues surrounding SVM classifier training have attracted increasing attention. In this paper, we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction, called Fed_DPDR-DPML, which greatly improves data utility while providing strong privacy guarantees. Considering in distributed learning scenarios, multiple participants usually hold unbalanced or small amounts of data. Therefore, Fed_DPDR-DPML enables multiple participants to collaboratively learn a global… More >

Dan Zhao¹, Yang You², Chuanwen Luo^3,*, Ting Chen^4,*, Yang Liu⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3063-3083, 2024, DOI:10.32604/cmes.2023.045400 - 11 March 2024

Abstract In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not… More >

Sanxiu Jiao¹, Lecai Cai^2,*, Jintao Meng³, Yue Zhao³, Kui Cheng²

Computer Systems Science and Engineering, Vol.48, No.1, pp. 247-265, 2024, DOI:10.32604/csse.2023.040194 - 26 January 2024

Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while More >

Sanxiu Jiao¹, Lecai Cai^2,*, Xinjie Wang¹, Kui Cheng², Xiang Gao³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1679-1694, 2024, DOI:10.32604/cmes.2023.030512 - 17 November 2023

Abstract As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection… More > Graphic Abstract

Muhammad Firdaus¹, Harashta Tatimma Larasati², Kyung-Hyune Rhee^3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2959-2978, 2023, DOI:10.32604/cmc.2023.039487 - 08 October 2023

Abstract The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks… More >

Mao Yuxin, Wang Honglin^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 863-878, 2023, DOI:10.32604/cmc.2023.034879 - 06 February 2023

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… More >