Lattice-Based Cryptography for Privacy Preserving Machine Learning

The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is Federated Learning (FL), which enables collaborative training of deep machine learning models among medical institutions by sharing model parameters instead of raw data. This study focuses on enhancing an existing privacy-preserving federated learning algorithm for medical data through the utilization of homomorphic encryption, building upon prior research. In contrast to the previous paper this work is based upon by Wibawa, using a single key for homomorphic encryption, our proposed solution is a practical implementation of a preprint by Ma Jing et. al. with a proposed encryption scheme (xMK-CKKS) for implementing multi-key homomorphic encryption. For this, our work first involves modifying a simple “ring learning with error” RLWE scheme. We then fork a popular FL framework for python where we integrate our own communication process with protocol buffers before we locate and modify the library’s existing training loop in order to further enhance the security of model updates with the multi-key homomorphic encryption scheme. Our experimental evaluations validate that despite these modifications, our proposed framework maintains robust model performance, as demonstrated by consistent metrics including validation accuracy, precision, f1-score, and recall

A Practical Implementation of Medical Privacy-Preserving Federated Learning Using Multi-Key Homomorphic Encryption and Flower Framework

The digitization of healthcare data has presented a pressing need to address privacy concerns within the realm of machine learning for healthcare institutions. One promising solution is federated learning, which enables collaborative training of deep machine learning models among medical institutions by sharing model parameters instead of raw data. This study focuses on enhancing an existing privacy-preserving federated learning algorithm for medical data through the utilization of homomorphic encryption, building upon prior research. In contrast to the previous paper, this work is based upon Wibawa, using a single key for HE, our proposed solution is a practical implementation of a preprint with a proposed encryption scheme (xMK-CKKS) for implementing multi-key homomorphic encryption. For this, our work first involves modifying a simple “ring learning with error” RLWE scheme. We then fork a popular federated learning framework for Python where we integrate our own communication process with protocol buffers before we locate and modify the library’s existing training loop in order to further enhance the security of model updates with the multi-key homomorphic encryption scheme. Our experimental evaluations validate that, despite these modifications, our proposed framework maintains a robust model performance, as demonstrated by consistent metrics including validation accuracy, precision, f1-score, and recall.publishedVersio

Analyse av data fra gjenoppliving av hjertestanspasienter - implementasjon i Python

I årrekker har det blitt utført forskning på hjertestanspasienter der livreddere under vanskelige forhold prøver gjenopplivning ved hjelp av hjerte-og lungeredning (HLR), defibrillator og intravenøse legemidler. Gjenopplivningen påvirker pasientens hjerterytmer på forskjellige måter, og kan utløse positive eller negative reaksjoner. Konsensus er at optimal HLR, altså 100-120 brystkompresjoner i minuttet på minst 5cm, øker oddsene for gjenoppliving av en hjertestanspasient. Dermed har det blitt utviklet hjertestartere med tilbakemelding hvor man på bakgrunn av signaler fra elektrodeputer og akselerometere kan veilede brukeren. Formålet med denne bacheloroppgaven var å utvikle et visningsprogram for å lese inn, bearbeidede, og visualisere signaler fra ulike hjertestansepisoder. Signalene blir representert i grafer med uthevede områder hvor det på forhånd har blitt utført automatisk deteksjon av hjerteslag, ventileringer osv. Programmet var opprinnelig skrevet i Matlab, men da Matlab krever lisens og har mindre utbredelse, så ble det besluttet å gå over til Python (versjon 3.8), som er et objektorientert programmeringsspråk med usedvanlig lettlest syntaks. Resultatet er et mye raskere program med fokus på robusthet og brukervennlighet. Vi håper dette forbedrer brukeropplevelsen og forenkler forskningsarbeid.For years research on cardiac arrest patients has been conducted, where medical personnel under duress try to resuscitate patients using cardiopulmonary resuscitation (CPR), defibrillator and intravenous medication. Resuscitation affects the patient’s heart rhythms in various ways and can have positive as well as negative effects. Consensus is that optimal CPR, that is 100-120 compressions per minute at a depth of at least 5cm, increases the odds of resuscitating a cardiac arrest patient. For that reason, there have been developed defibrillators with feedback, which analyse signals obtained through electrode pads and accelerometers, providing guidance to the user. The purpose of this bachelors thesis, was to create a tool to read, process and visualize signals from different recorded episodes of cardiac arrest. The signals are represented in graphs including automatically detected region of interests such as heartbeats, ventilation etc. The program was initially written in Matlab, but as Matlab requires a license and is less widespread, it was decided to implement a python-based version of the program. Python is an object oriented programming language with an easy to learn syntax. The result is a much faster program with focus on robustness and ease of use. We hope this can improve the user experience and reduce some of the research workload