Preserving the Privacy of Medical Data using Homomorphic Encryption and Prediction of Heart Disease using K-Nearest Neighbor

Behera, Sagarika; Rekha, B; Pandey, Pragya; Vidya, B; Prathuri, Jhansi Rani

doi:10.1109/icdsis55133.2022.9915983

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…As the popularity of ML increases in the cloud computing environment, privacy and security of the data become a great concern for cloud users. To overcome these problems, the researchers have given different methods for the application of homomorphic encryption in machine learning in the paper [18][19][20][21][22][23].…”

Section: Relevant Workmentioning

confidence: 99%

“…In this work, the Euclidean distance method is used to calculate the distance. The Euclidean distance formulae are shown in (23).…”

Section: Normalization Processmentioning

confidence: 99%

“…1: Compute the distance between the new data point X u and every data point in the training dataset. The commonly used distance metric is Euclidean distance given in (23). 2: Sort the distances in ascending order and select the K data points (k neighbors) with the smallest distances to X u .…”

Section: Algorithm 1 Knn Algorithm For Classificationmentioning

confidence: 99%

“…There are different distance calculation algorithms commonly used in KNN classification such as Euclidean distance, Manhattan distance, Minkowski distance, and Cosine similarity.In this work, the Euclidean distance method is used to calculate the distance. The Euclidean distance formulae are shown in(23).…”

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confidence: 99%

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FPGA-Based Acceleration of K-Nearest Neighbor Algorithm on Fully Homomorphic Encrypted Data

Behera,

Prathuri

2024

Cryptography

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The suggested solution in this work makes use of the parallel processing capability of FPGA to enhance the efficiency of the K-Nearest Neighbor (KNN) algorithm on encrypted data. The suggested technique was assessed utilizing the breast cancer datasets and the findings indicate that the FPGA-based acceleration method provides significant performance improvements over software implementation. The Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme is used for the computation of ciphertext. After extensive simulation in Python and implementation in FPGA, it was found that the proposed architecture brings down the computational time of KNN on ciphertext to a realistic value in the order of the KNN classification algorithm over plaintext. For the FPGA implementation, we used the Intel Agilex7 FPGA (AGFB014R24B2E2V) development board and validated the speed of computation, latency, throughput, and logic utilization. It was observed that the KNN on encrypted data has a computational time of 41.72 ms which is 80 times slower than the KNN on plaintext whose computational time is of 0.518 ms. The main computation time for CKKS FHE schemes is 41.72 ms. With our architecture, we were able to reduce the calculation time of the CKKS-based KNN to 0.85 ms by using 32 parallel encryption hardware and reaching 300 MHz speed.

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Section: Relevant Workmentioning

confidence: 99%

“…In this work, the Euclidean distance method is used to calculate the distance. The Euclidean distance formulae are shown in (23).…”

Section: Normalization Processmentioning

confidence: 99%

Section: Algorithm 1 Knn Algorithm For Classificationmentioning

confidence: 99%

mentioning

confidence: 99%

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FPGA-Based Acceleration of K-Nearest Neighbor Algorithm on Fully Homomorphic Encrypted Data

Behera,

Prathuri

2024

Cryptography

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“…In this system, PHE is used for kernel function computation, secure multiplication, and secure comparison to ensure privacy preserving. In [ 30 ], an RSA cryptosystem with a homomorphic encryption requirement is used to encrypt medical data before uploading it to the cloud, where the k-nearest neighbor algorithm is applied to perform certain operations on encrypted data, like addition and multiplication. However, homomorphic encryption is applied to shallow learning ML algorithms, and its use in deep learning algorithms presents some limitations.…”

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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