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Journal of Econometrics Aug 2023Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public...
Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy protection and statistical accuracy. In this work, we mitigate this trade-off by developing a distribution-invariant privatization (DIP) method to reconcile both high statistical accuracy and strict differential privacy. As a result, any downstream statistical or machine learning task yields essentially the same conclusion as if one used the original data. Numerically, under the same strictness of privacy protection, DIP achieves superior statistical accuracy in a wide range of simulation studies and real-world benchmarks.
PubMed: 37701878
DOI: 10.1016/j.jeconom.2022.05.004 -
The Journal of Adolescent Health :... Oct 2023
Topics: Humans; Parental Consent; Privacy; Relational Autonomy
PubMed: 37716714
DOI: 10.1016/j.jadohealth.2023.07.004 -
The American Journal of Nursing Aug 2023The benefits and consequences of patient access to health records.
The benefits and consequences of patient access to health records.
Topics: Humans; Medical Records Systems, Computerized; Patient Access to Records
PubMed: 37498015
DOI: 10.1097/01.NAJ.0000947376.76540.20 -
Nature Mar 2024Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics. The All of Us Research...
Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.
Topics: Humans; Access to Information; Black People; Datasets as Topic; Electronic Health Records; Ethnicity; European People; Genetic Predisposition to Disease; Genetic Variation; Genetics, Medical; Genetics, Population; Genome, Human; Genomics; Longitudinal Studies; Minority Groups; Racial Groups; Reproducibility of Results; Research Personnel; Time Factors; Vulnerable Populations
PubMed: 38374255
DOI: 10.1038/s41586-023-06957-x -
Medecine Sciences : M/S Oct 2023Advanced analysis of environmental DNA for diversity monitoring using deep sequencing reveals the presence of human DNA in many samples connected to human...
Advanced analysis of environmental DNA for diversity monitoring using deep sequencing reveals the presence of human DNA in many samples connected to human activity.Moreover, this DNA is in relatively good condition and can be used for genetic survey of populations and even individuals. This opens many interesting scientific opportunities but also raises serious privacy issues.
Topics: Humans; DNA; DNA, Environmental; Genetic Privacy
PubMed: 37943139
DOI: 10.1051/medsci/2023111 -
The Lancet. Digital Health Dec 2023The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology,... (Review)
Review
The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.
Topics: Humans; Ophthalmology; Artificial Intelligence; Language; Medicine; Privacy
PubMed: 38000875
DOI: 10.1016/S2589-7500(23)00201-7 -
EMBO Reports Jul 2023The EMBO Journal and EMBO Reports join EMBO Molecular Medicine, Molecular Systems Biology and Life Science Alliance as Open Access journals from 2024. Full Open Access...
The EMBO Journal and EMBO Reports join EMBO Molecular Medicine, Molecular Systems Biology and Life Science Alliance as Open Access journals from 2024. Full Open Access at EMBO Press completes another step towards the goal of an integrated Open Science approach for the dissemination of highly selected and curated science.
Topics: Access to Information; Biological Science Disciplines
PubMed: 37382563
DOI: 10.15252/embr.202357638 -
Genetic Testing and Molecular Biomarkers Sep 2023
Topics: Humans; Privacy; Genetic Testing; Genetic Privacy; Confidentiality
PubMed: 37702624
DOI: 10.1089/gtmb.2023.29076.persp -
Journal of the American Psychoanalytic... Oct 2023
Topics: Humans; Privacy; Writing; Sexual Behavior
PubMed: 38140968
DOI: 10.1177/00030651231208340 -
Nature Protocols Oct 2023The ability to record and alter brain activity by using implantable and nonimplantable neural devices, while poised to have significant scientific and clinical benefits,... (Review)
Review
The ability to record and alter brain activity by using implantable and nonimplantable neural devices, while poised to have significant scientific and clinical benefits, also raises complex ethical concerns. In this Perspective, we raise awareness of the ability of artificial intelligence algorithms and data-aggregation tools to decode and analyze data containing highly sensitive information, jeopardizing personal neuroprivacy. Voids in existing regulatory frameworks, in fact, allow unrestricted decoding and commerce of neurodata. We advocate for the implementation of proposed ethical and human rights guidelines, alongside technical options such as data encryption, differential privacy and federated learning to ensure the protection of neurodata privacy. We further encourage regulatory bodies to consider taking a position of responsibility by categorizing all brain-derived data as sensitive health data and apply existing medical regulations to all data gathered via pre-registered neural devices. Lastly, we propose that a technocratic oath may instill a deontology for neurotechnology practitioners akin to what the Hippocratic oath represents in medicine. A conscientious societal position that thoroughly rejects the misuse of neurodata would provide the moral compass for the future development of the neurotechnology field.
Topics: Humans; Privacy; Artificial Intelligence; Hippocratic Oath; Algorithms
PubMed: 37697107
DOI: 10.1038/s41596-023-00873-0