176 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
A Systematic Literature Review of Individuals\u27 Perspectives on Privacy and Genetic Information in the United States
Concerns about genetic privacy affect individuals\u27 willingness to accept genetic testing in clinical care and to participate in genomics research. To learn what is already known about these views, we conducted a systematic review, which ultimately analyzed 53 studies involving the perspectives of 47,974 participants on real or hypothetical privacy issues related to human genetic data. Bibliographic databases included MEDLINE, Web of Knowledge, and Sociological Abstracts. Three investigators independently screened studies against predetermined criteria and assessed risk of bias. The picture of genetic privacy that emerges from this systematic literature review is complex and riddled with gaps. When asked specifically are you worried about genetic privacy, the general public, patients, and professionals frequently said yes. In many cases, however, that question was posed poorly or only in the most general terms. While many participants expressed concern that genomic and medical information would be revealed to others, respondents frequently seemed to conflate privacy, confidentiality, control, and security. People varied widely in how much control they wanted over the use of data. They were more concerned about use by employers, insurers, and the government than they were about researchers and commercial entities. In addition, people are often willing to give up some privacy to obtain other goods. Importantly, little attention was paid to understanding the factor-sociocultural, relational, and media - that influence people\u27s opinions and decisions. Future investigations should explore in greater depth which concerns about genetic privacy are most salient to people and the social forces and contexts that influence those perceptions. It is also critical to identify the social practices that will make the collection and use of these data more trustworthy for participants as well as to identify the circumstances that lead people to set aside worries and decide to participate in research
The Luminosity Function of Young Star Clusters In "The Antennae" Galaxies (NGC 4038/4039)
The WFPC2 of the HST has been used to obtain high-resolution images of NGC
4038/4039 that go roughly 3 magnitudes deeper in V than previous observations
made during Cycle 2 (-14 < M_V < -6). To first order the luminosity function
(LF) is a power law, with exponent \alpha = -2.12 +/- 0.04. However, after
decoupling the cluster and stellar LFs, which overlap in the range -9 < M_V <
-6, we find an apparent bend in the young cluster LF at approximately M_V =
-10.4. The LF has a power law exponent -2.6 +/- 0.2 in the brightward and -1.7
+/- 0.2 in the faintward. The bend corresponds to a mass ~ 10^5 M_{\odot}, only
slightly lower than the characteristic mass of globular clusters in the Milky
Way (~2x10^5 M_{\odot}). The star clusters of the Antennae appear slightly
resolved, with median effective radii of 4 +/- 1 pc, similar to or perhaps
slightly larger than those of globular clusters in our Galaxy. However, the
radial extents of some of the very young clusters (ages < 10 Myr) are much
larger than those of old globular clusters. A combination of the UBVI colors,
\Halpha morphology, and GHRS spectra enables us to age-date the clusters in
different regions of The Antennae. We find two groups of young star clusters
with ages <~ 20Myr and ~100Myr, as well as an intermediate-age group (~500 Myr)
and a handful of old globular clusters from the progenitor galaxies. Age
estimates derived from GHRS spectroscopy yield 3 +/- 1 Myr for Knot K (just
south of the nucleus of NGC 4038) and 7 +/- 1 Myr for Knot S in the Western
Loop, in good agreement with ages derived from the UBVI colors. Effective
gas-outflow velocities from Knots S and K are estimated to be about 25-30 km/s.
However, the measured widths of the interstellar absorption lines suggest
dispersion velocities of ~400 km/s along the lines of sight to Knots S and K.Comment: 56 pages, 4 tables and 23 figures, texts in AAS style, to be
published in A
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Deep learning continues to rapidly evolve and is now demonstrating remarkable
potential for numerous medical prediction tasks. However, realizing deep
learning models that generalize across healthcare organizations is challenging.
This is due, in part, to the inherent siloed nature of these organizations and
patient privacy requirements. To address this problem, we illustrate how split
learning can enable collaborative training of deep learning models across
disparate and privately maintained health datasets, while keeping the original
records and model parameters private. We introduce a new privacy-preserving
distributed learning framework that offers a higher level of privacy compared
to conventional federated learning. We use several biomedical imaging and
electronic health record (EHR) datasets to show that deep learning models
trained via split learning can achieve highly similar performance to their
centralized and federated counterparts while greatly improving computational
efficiency and reducing privacy risks
Risk of re-identification for shared clinical speech recordings
Large, curated datasets are required to leverage speech-based tools in
healthcare. These are costly to produce, resulting in increased interest in
data sharing. As speech can potentially identify speakers (i.e., voiceprints),
sharing recordings raises privacy concerns. We examine the re-identification
risk for speech recordings, without reference to demographic or metadata, using
a state-of-the-art speaker recognition system. We demonstrate that the risk is
inversely related to the number of comparisons an adversary must consider,
i.e., the search space. Risk is high for a small search space but drops as the
search space grows ( for comparisons, comparisons). Next, we show that the nature of a speech
recording influences re-identification risk, with non-connected speech (e.g.,
vowel prolongation) being harder to identify. Our findings suggest that speaker
recognition systems can be used to re-identify participants in specific
circumstances, but in practice, the re-identification risk appears low.Comment: 24 pages, 6 figure
A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models
Synthetic health data have the potential to mitigate privacy concerns when
sharing data to support biomedical research and the development of innovative
healthcare applications. Modern approaches for data generation based on machine
learning, generative adversarial networks (GAN) methods in particular, continue
to evolve and demonstrate remarkable potential. Yet there is a lack of a
systematic assessment framework to benchmark methods as they emerge and
determine which methods are most appropriate for which use cases. In this work,
we introduce a generalizable benchmarking framework to appraise key
characteristics of synthetic health data with respect to utility and privacy
metrics. We apply the framework to evaluate synthetic data generation methods
for electronic health records (EHRs) data from two large academic medical
centers with respect to several use cases. The results illustrate that there is
a utility-privacy tradeoff for sharing synthetic EHR data. The results further
indicate that no method is unequivocally the best on all criteria in each use
case, which makes it evident why synthetic data generation methods need to be
assessed in context
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