6,581 research outputs found
Privacy in the Genomic Era
Genome sequencing technology has advanced at a rapid pace and it is now
possible to generate highly-detailed genotypes inexpensively. The collection
and analysis of such data has the potential to support various applications,
including personalized medical services. While the benefits of the genomics
revolution are trumpeted by the biomedical community, the increased
availability of such data has major implications for personal privacy; notably
because the genome has certain essential features, which include (but are not
limited to) (i) an association with traits and certain diseases, (ii)
identification capability (e.g., forensics), and (iii) revelation of family
relationships. Moreover, direct-to-consumer DNA testing increases the
likelihood that genome data will be made available in less regulated
environments, such as the Internet and for-profit companies. The problem of
genome data privacy thus resides at the crossroads of computer science,
medicine, and public policy. While the computer scientists have addressed data
privacy for various data types, there has been less attention dedicated to
genomic data. Thus, the goal of this paper is to provide a systematization of
knowledge for the computer science community. In doing so, we address some of
the (sometimes erroneous) beliefs of this field and we report on a survey we
conducted about genome data privacy with biomedical specialists. Then, after
characterizing the genome privacy problem, we review the state-of-the-art
regarding privacy attacks on genomic data and strategies for mitigating such
attacks, as well as contextualizing these attacks from the perspective of
medicine and public policy. This paper concludes with an enumeration of the
challenges for genome data privacy and presents a framework to systematize the
analysis of threats and the design of countermeasures as the field moves
forward
Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective
Rapid advances in human genomics are enabling researchers to gain a better
understanding of the role of the genome in our health and well-being,
stimulating hope for more effective and cost efficient healthcare. However,
this also prompts a number of security and privacy concerns stemming from the
distinctive characteristics of genomic data. To address them, a new research
community has emerged and produced a large number of publications and
initiatives.
In this paper, we rely on a structured methodology to contextualize and
provide a critical analysis of the current knowledge on privacy-enhancing
technologies used for testing, storing, and sharing genomic data, using a
representative sample of the work published in the past decade. We identify and
discuss limitations, technical challenges, and issues faced by the community,
focusing in particular on those that are inherently tied to the nature of the
problem and are harder for the community alone to address. Finally, we report
on the importance and difficulty of the identified challenges based on an
online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies
(PoPETs), Vol. 2019, Issue
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations
The projected increase of genotyping in the clinic and the rise of large
genomic databases has led to the possibility of using patient medical data to
perform genomewide association studies (GWAS) on a larger scale and at a lower
cost than ever before. Due to privacy concerns, however, access to this data is
limited to a few trusted individuals, greatly reducing its impact on biomedical
research. Privacy preserving methods have been suggested as a way of allowing
more people access to this precious data while protecting patients. In
particular, there has been growing interest in applying the concept of
differential privacy to GWAS results. Unfortunately, previous approaches for
performing differentially private GWAS are based on rather simple statistics
that have some major limitations. In particular, they do not correct for
population stratification, a major issue when dealing with the genetically
diverse populations present in modern GWAS. To address this concern we
introduce a novel computational framework for performing GWAS that tailors
ideas from differential privacy to protect private phenotype information, while
at the same time correcting for population stratification. This framework
allows us to produce privacy preserving GWAS results based on two of the most
commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based
statistics. We test our differentially private statistics, PrivSTRAT and
PrivLMM, on both simulated and real GWAS datasets and find that they are able
to protect privacy while returning meaningful GWAS results.Comment: To be presented at RECOMB 201
Routes for breaching and protecting genetic privacy
We are entering the era of ubiquitous genetic information for research,
clinical care, and personal curiosity. Sharing these datasets is vital for
rapid progress in understanding the genetic basis of human diseases. However,
one growing concern is the ability to protect the genetic privacy of the data
originators. Here, we technically map threats to genetic privacy and discuss
potential mitigation strategies for privacy-preserving dissemination of genetic
data.Comment: Draft for comment
Health privacy : methods for privacy-preserving data sharing of methylation, microbiome and eye tracking data
This thesis studies the privacy risks of biomedical data and develops mechanisms for privacy-preserving data sharing. The contribution of this work is two-fold: First, we demonstrate privacy risks of a variety of biomedical data types such as DNA methylation data, microbiome data and eye tracking data. Despite being less stable than well-studied genome data and more prone to environmental changes, well-known privacy attacks can be adopted and threaten the privacy of data donors. Nevertheless, data sharing is crucial to advance biomedical research given that collection the data of a sufficiently large population is complex and costly. Therefore, we develop as a second step privacy- preserving tools that enable researchers to share such biomedical data. and second, we equip researchers with tools to enable privacy-preserving data sharing. These tools are mostly based on differential privacy, machine learning techniques and adversarial examples and carefully tuned to the concrete use case to maintain data utility while preserving privacy.Diese Dissertation beleuchtet Risiken für die Privatsphäre von biomedizinischen Daten und entwickelt Mechanismen für privatsphäre-erthaltendes Teilen von Daten. Dies zerfällt in zwei Teile: Zunächst zeigen wir die Risiken für die Privatsphäre auf, die von biomedizinischen Daten wie DNA Methylierung, Mikrobiomdaten und bei der Aufnahme von Augenbewegungen vorkommen. Obwohl diese Daten weniger stabil sind als Genomdaten, deren Risiken der Forschung gut bekannt sind, und sich mehr unter Umwelteinflüssen ändern, können bekannte Angriffe angepasst werden und bedrohen die Privatsphäre der Datenspender. Dennoch ist das Teilen von Daten essentiell um biomedizinische Forschung voranzutreiben, denn Daten von einer ausreichend großen Studienpopulation zu sammeln ist aufwändig und teuer. Deshalb entwickeln wir als zweiten Schritt privatsphäre-erhaltende Techniken, die es Wissenschaftlern erlauben, solche biomedizinischen Daten zu teilen. Diese Techniken basieren im Wesentlichen auf differentieller Privatsphäre und feindlichen Beispielen und sind sorgfältig auf den konkreten Einsatzzweck angepasst um den Nutzen der Daten zu erhalten und gleichzeitig die Privatsphäre zu schützen
Supporting Regularized Logistic Regression Privately and Efficiently
As one of the most popular statistical and machine learning models, logistic
regression with regularization has found wide adoption in biomedicine, social
sciences, information technology, and so on. These domains often involve data
of human subjects that are contingent upon strict privacy regulations.
Increasing concerns over data privacy make it more and more difficult to
coordinate and conduct large-scale collaborative studies, which typically rely
on cross-institution data sharing and joint analysis. Our work here focuses on
safeguarding regularized logistic regression, a widely-used machine learning
model in various disciplines while at the same time has not been investigated
from a data security and privacy perspective. We consider a common use scenario
of multi-institution collaborative studies, such as in the form of research
consortia or networks as widely seen in genetics, epidemiology, social
sciences, etc. To make our privacy-enhancing solution practical, we demonstrate
a non-conventional and computationally efficient method leveraging distributing
computing and strong cryptography to provide comprehensive protection over
individual-level and summary data. Extensive empirical evaluation on several
studies validated the privacy guarantees, efficiency and scalability of our
proposal. We also discuss the practical implications of our solution for
large-scale studies and applications from various disciplines, including
genetic and biomedical studies, smart grid, network analysis, etc
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