Privacy-preserving Naive Bayes classification in semi-fully distributed data model

Vu, Duy-Hien

doi:10.1016/j.cose.2022.102630

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…Recently, training/learning high-quality AI models from imbalanced data in real-time applications has become a popular research topic [333]. Vu et al [334] developed a novel collaborative data model for semi-fully distributed settings for real-time medical applications. The proposed model employs the Naive Bayes classification to provide both privacy and accuracy in many real-life applications.…”

Section: B Potential Opportunities For Future Research In Privacy Domainmentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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Section: B Potential Opportunities For Future Research In Privacy Domainmentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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“…The stronger the connection between two units, the greater their weight. The information that we want to process is placed at the first layer of units, and the output of each neuron may be an input to another neuron, and each unit has a fictitious input whose value is equal to one, transmitted through a link that is also loaded with an initial weight [24,25].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Network Traffic Prediction Based on Boosting Learning

Kashmoola

Ahmed

Alsaleem

2022

eijs

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Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traffic patterns that can be categorized based on statistical characteristics. These methods help determine the type of traffic and protect user privacy at the same time. To classify encrypted traffic from end to end, this paper proposes using (XGboost) algorithms, finding the highest parameters using Bayesian optimization, and comparing the proposed model with machine learning algorithms (Nearest Neighbor, Logistic Regression, Decision Trees, Naive Bayes, Multilayer Neural Networks) to classify traffic from end to end. Network traffic has two classifications: whether the traffic is encrypted or not, and the target application. The research results showed the possibility of classifying dual and multiple traffic with high accuracy. The proposed model has a higher classification accuracy than the other models, and finding the optimal parameters increases the model accuracy.

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“…However, this paper has not done in-depth research on feature analysis. Vu [6] proposed a new NB classification scheme, which is realized by protecting privacy through secure multi-party computing. The classification scheme can effectively ensure the accuracy of the classification model.…”

Section: Introductionmentioning

confidence: 99%

Quantitative feature classification for breast ultrasound images using improved naive bayes

Sang

et al. 2022

IET Image Processing

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The quantitative feature classification of breast ultrasound images can explore the intrinsic connection between image features and lesions, and become an important basis for the diagnosis of tumor nature in clinical medicine. However, the tumor features extracted from breast ultrasound images are generally based on the overall situation, which has an impact on the classification of features in ultrasound images. Therefore, a quantitative feature classification algorithm for breast ultrasound images using improved Naive Bayes (NB) is proposed. First, histogram features, grayscale co‐generation matrix and other texture features are extracted from the ultrasound image. Second, NB is combined with decision tree calculation to improve the traditional NB algorithm. Finally, feature classification is optimized using improved NB algorithm to achieve high accuracy in the classification of ultrasound images. The results show that the contour of the lesion recognition results obtained by the proposed algorithm is the most complete, and the other regions can maximize the retention of the texture features of the original image. The classification accuracy is high, and the classification time of quantitative features of breast ultrasound images is short. It has application value in the field of breast ultrasound diagnosis.

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Privacy-preserving Naive Bayes classification in semi-fully distributed data model

Cited by 31 publications

References 17 publications

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Network Traffic Prediction Based on Boosting Learning

Quantitative feature classification for breast ultrasound images using improved naive bayes

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