BFV-Based Homomorphic Encryption for Privacy-Preserving CNN Models

Wibawa, Febrianti; Çatak, Ferhat Özgür; Sarp, Salih; Kuzlu, Murat

doi:10.3390/cryptography6030034

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…Cryptographic methods are widely used in several FL methods to preserve data privacy when exchanging intermediate parameters during the FL training process [117][118][119]. Similarly to Cui et al [116], whose work falls into the smart healthcare domain, Zhang et al [120] presented an FL mechanism for the IoHT.…”

Section: Cryptographic Methodsmentioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.

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Section: Cryptographic Methodsmentioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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“…This work uses a simple neural network with the HELib algorithm on MNIST and heart disease datasets for evaluation. The privacy-preserving CNN models with BFV homomorphic encryption are demonstrated by F. Wibawa et al [14]. A secure multi-party protocol is used for deep learning model protection at each client/hospital, which collaborates by federated learning and evaluates by aggregating servers.…”

Section: Literature Surveymentioning

confidence: 99%

“…Multi-Party Computation [14,16,19,25,28], Client-Server Model [14,17], ETL Process for Database [20], Multi-Key Homomorphic Encryption [25,26], Federated Learning [27], and Multi-Tenancy Environment [30].…”

Section: Institutional Reviewmentioning

confidence: 99%

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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“…In another study, the authors proposed a privacy-preserving federated learning system based on homomorphic encryption for medical data [7]. Due to of the high number of privacy attacks on deep learning models, homomorphic encryption-based model protection from the adversary collaborator was used to solve this problem.…”

Section: Summary Of the Special Issuementioning

confidence: 99%

“…Due to of the high number of privacy attacks on deep learning models, homomorphic encryption-based model protection from the adversary collaborator was used to solve this problem. The main contributions of [7] are as follows. They provide a practical method to implement secure multi-party computation in federated learning to improve the privacy and security of medical data, which can protect the confidentiality of the sensitive medical data.…”

Section: Summary Of the Special Issuementioning

confidence: 99%