Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Wood, Alexander; Najarian, Kayvan; Kahrobaei, Delaram

doi:10.1145/3394658

Cited by 106 publications

(43 citation statements)

References 146 publications

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“…This process of analysis should be iterative until the best compromise between utility and privacy is reached. However, other privacy-preserving approaches have been proposed in the literature, like homomorphic encryption [ 28 , 29 ] or differential privacy [ 30 ], that may not necessarily hamper the data analysis.…”

Section: Discussionmentioning

confidence: 99%

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Fundamental privacy rights in a pandemic state

Carvalho

Faria²,

Antunes

et al. 2021

PLoS ONE

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Faced with the emergence of the Covid-19 pandemic, and to better understand and contain the disease’s spread, health organisations increased the collaboration with other organisations sharing health data with data scientists and researchers. Data analysis assists such organisations in providing information that could help in decision-making processes. For this purpose, both national and regional health authorities provided health data for further processing and analysis. Shared data must comply with existing data protection and privacy regulations. Therefore, a robust de-identification procedure must be used, and a re-identification risk analysis should also be performed. De-identified data embodies state-of-the-art approaches in Data Protection by Design and Default because it requires the protection of direct and indirect identifiers (not just direct). This article highlights the importance of assessing re-identification risk before data disclosure by analysing a data set of individuals infected by Covid-19 that was made available for research purposes. We stress that it is highly important to make this data available for research purposes and that this process should be based on the state of the art methods in Data Protection by Design and by Default. Our main goal is to consider different re-identification risk analysis scenarios since the information on the intruder side is unknown. Our conclusions show that there is a risk of identity disclosure for all of the studied scenarios. For one, in particular, we proceed to an example of a re-identification attack. The outcome of such an attack reveals that it is possible to identify individuals with no much effort.

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Section: Discussionmentioning

confidence: 99%

“…However, other privacy-preserving approaches have been proposed in the literature, like homomorphic encryption [28,29] or differential privacy [30], that may not necessarily hamper the data analysis.…”

Section: Plos Onementioning

confidence: 99%

Fundamental privacy rights in a pandemic state

Carvalho

Faria²,

Antunes

et al. 2021

PLoS ONE

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show abstract

“…al. survey ML applications for medicine and bioinformatics fields and discuss common solutions for secure GWAS [ 28 ]. Logistic regression models and usage of statistical scores like χ 2 have been extensively used in this domain.…”

Section: Methodsmentioning

confidence: 99%

Fast and Scalable Private Genotype Imputation Using Machine Learning and Partially Homomorphic Encryption

et al. 2021

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The recent advances in genome sequencing technologies provide unprecedented opportunities to understand the relationship between human genetic variation and diseases. However, genotyping whole genomes from a large cohort of individuals is still cost prohibitive. Imputation methods to predict genotypes of missing genetic variants are widely used, especially for genome-wide association studies. Accurate genotype imputation requires complex statistical methods. Due to the data and computingintensive nature of the problem, imputation is increasingly outsourced, raising serious privacy concerns. In this work, we investigate solutions for fast, scalable, and accurate privacy-preserving genotype imputation using Machine Learning (ML) and a standardized homomorphic encryption scheme, Paillier cryptosystem. ML-based privacy-preserving inference has been largely optimized for computation-heavy non-linear functions in a single-output multi-class classification setting. However, having a large number of multiclass outputs per genome per individual calls for further optimizations and/or approximations specific to this application. Here we explore the effectiveness of linear models for genotype imputation to convert them to privacy-preserving equivalents using standardized homomorphic encryption schemes. Our results show that performance of our privacy-preserving genotype imputation method is equivalent to the state-of-theart plaintext solutions, achieving up to 99% micro area under curve score, even on real-world large-scale datasets upto 80,000 targets.INDEX TERMS Genotype imputation, machine learning, privacy-preserving computation.

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“…Both approaches have common and exclusive interesting properties that deal with the BTP challenges and the tradeoffs. There are several surveys that investigate either Bloom filter [4], [5] or homomorphic encryption [6]- [8] and their applications in general. However, none of them focuses on examining these two approaches from a biometrics point of view.…”

Section: Introductionmentioning

confidence: 99%

Bloom Filter vs Homomorphic Encryption: Which approach protects the biometric data and satisfies ISO/IEC 24745?

Bassit

Hahn

Zeinstra

et al. 2021

2021 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Bloom filter (BF) and homomorphic encryption (HE) are popular modern techniques used to design biometric template protection (BTP) schemes that aim to protect the sensitive biometric information during storage and the comparison process. However, in practice, many BTP schemes based on BF or HE violate at least one of the privacy requirements of the international standard ISO/IEC 24745: irreversibility, unlinkability and confidentiality. In this paper, we investigate the state-of-the-art BTP schemes based on these two approaches and assess their relative strengths and weaknesses with respect to the three requirements of ISO/IEC 24745. The results of our investigation showed that the choice between BF and HE depends on the setting where the BTP scheme will be deployed and the level of trustworthiness of the parties involved in processing the protected template. As a result, HE enhanced by verifiable computation techniques can satisfy the privacy requirements of ISO/IEC 24745 in a trustless setting.

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Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Cited by 106 publications

References 146 publications

Fundamental privacy rights in a pandemic state

Fundamental privacy rights in a pandemic state

Fast and Scalable Private Genotype Imputation Using Machine Learning and Partially Homomorphic Encryption

Bloom Filter vs Homomorphic Encryption: Which approach protects the biometric data and satisfies ISO/IEC 24745?

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