Privacy-Preserving Multi-Class Support Vector Machine Model on Medical Diagnosis

Chen, Yange; Mao, Qinyu; Wang, Baocang; Duan, Pu; Zhang, Benyu; Hong, Zhiyong

doi:10.1109/jbhi.2022.3157592

Cited by 33 publications

(14 citation statements)

References 44 publications

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“…This paper [10] presents a medical diagnostics method that protects patients' privacy and is based on multi-class support vector machines (SVMs). Both the distributed two trapdoors public key cryptosystem (DT-PKC) and the Boneh-Goh-Nissim (BGN) cryptosystem are the foundation for this approach.…”

Section: Related Workmentioning

confidence: 99%

NegML: A Privacy-Preserving Machine Learning Approach Based on Negative Database

2023

IJETER

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Machine learning has become an increasingly prominent subject in the age of big data. It has made significant advances in image identification, object detection, and natural language processing, among other areas. The initial aim of machine learning is to extract meaningful information from enormous amounts of data, which unavoidably raises privacy concerns. Numerous privacy-preserving machine-learning approaches have been presented so far. However, most of them suffer from significant improvements in efficiency or accuracy. A negative database (NDB) is a data representation that may safeguard data privacy by storing and exploiting the complementary form of original data. In this research, we provide NegML, a privacy-preserving machine learning approach based on NDB. Private data are first transformed to NDB before being fed into machine learning algorithms such as a Multilayer perceptron (MLP), Logistic regression (LR), Gaussian naive Bayes (GNB), Decision tree (DT), as well as Random forest (RF). NegML has the same computational complexity as the original machine learning algorithms without privacy protection. Experiment findings on heart illnesses, milk datasets, Car evaluation benchmark datasets and Blood fusion dataset show that the accuracy of NegML is equivalent to the original machine learning model in most circumstances, as well as the technique based on differential privacy.

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Section: Related Workmentioning

confidence: 99%

NegML: A Privacy-Preserving Machine Learning Approach Based on Negative Database

2023

IJETER

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“…SVM is used in a variety of studies in the field of basic science and medicine, including clinical data analysis, laboratory testing for detection of disease and clinical trials of medicines [77][78][79]. In this study, we developed a very reliable method for predicting aromataserelated proteins, based on a variety of protein patterns such as AAC, DPC and Hybrid approaches.…”

Section: Plos Onementioning

confidence: 99%

Computational method for aromatase-related proteins using machine learning approach

Selvaraj

Kaur

2023

PLoS ONE

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Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

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“…Homomorphic encryption is implemented within ML algorithms to perform operations directly on encrypted data to ensure the confidentiality of the data. Several models of secure SVM using homomorphic cryptography are proposed to design secure clinical diagnosis [ 27 , 28 ]. B. Xie et al [ 29 ] propose a secure system for online diagnosis based on multiclass SVM placed on the cloud.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition

Castro,

Impedovo,

Pirlo

2023

Sensors

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Human activity recognition (HAR) through gait analysis is a very promising research area for early detection of neurodegenerative diseases because gait abnormalities are typical symptoms of some neurodegenerative diseases, such as early dementia. While working with such biometric data, the performance parameters must be considered along with privacy and security issues. In other words, such biometric data should be processed under specific security and privacy requirements. This work proposes an innovative hybrid protection scheme combining a partially homomorphic encryption scheme and a cancelable biometric technique based on random projection to protect gait features, ensuring patient privacy according to ISO/IEC 24745. The proposed hybrid protection scheme has been implemented along a long short-term memory (LSTM) neural network to realize a secure early dementia diagnosis system. The proposed protection scheme is scalable and implementable with any type of neural network because it is independent of the network’s architecture. The conducted experiments demonstrate that the proposed protection scheme enables a high trade-off between safety and performance. The accuracy degradation is at most 1.20% compared with the early dementia recognition system without the protection scheme. Moreover, security and computational analyses of the proposed scheme have been conducted and reported.

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Privacy-Preserving Multi-Class Support Vector Machine Model on Medical Diagnosis

Cited by 33 publications

References 44 publications

NegML: A Privacy-Preserving Machine Learning Approach Based on Negative Database

NegML: A Privacy-Preserving Machine Learning Approach Based on Negative Database

Computational method for aromatase-related proteins using machine learning approach

A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition

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