Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation

Son, Yongha; Han, Kyoung-Sik; Lee, Yong Seok; Yu, Jonghan; Im, Young‐Hyuck; Shin, Seungwon

doi:10.1371/journal.pone.0260681

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…These approaches formulated the appropriate structure and levels of matrix noise for privacy-aware matrix-valued data publishing [ 68 , 69 ]. It should be noted that matrix masking has been utilized in previous studies for implementing secure protocols [ 55 , 71 ] but are not available for developers in an easy-to-use manner. Currently, COLLAGENE implements mask matrix generation using Gaussian-valued noise by default that can be used for masking encrypted matrices.…”

Section: Resultsmentioning

confidence: 99%

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Li,

Kim,

Zhang

et al. 2023

Genome Biol

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Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935.

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Section: Resultsmentioning

confidence: 99%

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Li,

Kim,

Zhang

et al. 2023

Genome Biol

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“…A secure two-party computation for cancer prediction using homomorphic encryption is demonstrated by Y. Son et al [16]. A gated recurrent unit (GRU) model is used to secure and compute over the encrypted data for homomorphic encryption to predict end-to-end recurrence.…”

Section: Literature Surveymentioning

confidence: 99%

“…Fast Matrix Multiplication [13], Convolutional/Deep Neural Network [23,24], Augmented Ensemble Learning [18], Genetic Algorithm [12], Gated Recurrent Unit [16], and Decision Tree [15].…”

Section: Institutional Reviewmentioning

confidence: 99%

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Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)

Sheu,

Lin,

Pardeshi

et al. 2023

Sensors

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The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient’s remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient’s affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient’s data/report while saving the travel and waiting time for the hospital services. The patient’s data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.

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“…With the development of medical research, there is a strong requirement for the quality of multicenter medical research, which has many significant advantages over single-center medical research, including sufficient data size, improved generalizability, and reproducibility of the research outcomes ( 1 ). Multicenter medical research aims to strengthen collaborations among institutions, promote new discoveries with pooled dataset from multiple sources, and accelerate the translation of research outcomes into clinical practice ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

High performance of privacy-preserving acute myocardial infarction auxiliary diagnosis based on federated learning: a multicenter retrospective study

Xu¹,

Yu²,

Lin³

et al. 2022

Ann Transl Med

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Background: Multicenter medical research is becoming a new trend. However, interagency data privacy security protection has become a major bottleneck of multicenter research. Therefore, to overcome this privacy protection issue, the aim of the present study was to apply a self-developed privacy-preserving machine learning framework for researchers who can build models on medical data from multiple sources, while providing privacy protection for both sensitive data and the learned model.Methods: Based on Arya, a novel privacy computing platform developed by Healink, we constructed a privacy-preserving federated learning (FL) model using the fully connected neural network with datasets from 2-3 individual medical institutions. In the dataset, 80% of records were used for joint modeling on acute myocardial infarction (AMI) diagnosis. Modeling efficacy was evaluated with the remaining 20% of records. As the control, 1,500 medical records from 1 medical institution were used for single-center modeling and efficacy evaluation. During the process, the original data were still kept in individual hospital without moving or transferring out of the hospitial. The diagnostic efficacy (sensitivity, positive predictive value, and accuracy) was evaluated.Results: Our privacy-preserving FL model gives reliable AMI diagnostic efficacy. Three-center modeling (79% sensitivity, 88% positive predictive value, and 82.3% accuracy) and two-center modeling (77.8% or 77.6% sensitivity, 86.7% or 85.30% positive predictive value, and 81% or 79.7% accuracy) achieved relative high diagnostic efficacy; and single-center modeling achieved relative low diagnostic efficacy (76% sensitivity, 84.7% positive predictive value, and 79% accuracy).

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Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation

Cited by 13 publications

References 37 publications

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)

High performance of privacy-preserving acute myocardial infarction auxiliary diagnosis based on federated learning: a multicenter retrospective study

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