Pipeline release! nf-core/detaxizer v1.2.0 - detaxizer 1.2.0 - Kunschd!
Please see the changelog: https://github.com/nf-core/detaxizer/releases/tag/1.2.0
#deidentification #decontamination #edna #fastq #filter #longreads #metabarcoding #metagenomics #microbiome #nanopore #shortreads #shotgun #taxonomicclassification #taxonomicprofiling #nfcore #openscience #nextflow #bioinformatics
As a first post I would like to not highlight my own research, but the research of my colleagues, since I really love the way how this research began: With a question many people have asked.
As our working group researches on topics on #anonymization and #deidentification of medical and health data, the risk of identifying people or their membership in certain groups (e.g. having a specific diagnosis) is always present. On one of our retreats they decided to research the question:
Who are those adversaries, trying to do re-identify people in health data sets, what are their motives and what could be the harm?
The results can be found in the #OpenAccess paper: Health Data Re-Identification: Assessing Adversaries and Potential Harms
https://doi.org/10.3233/SHTI240626
#data #adversary #healthdata #medicalinformatics #privacy #dataprotection
Pipeline release! nf-core/detaxizer v1.1.0 - detaxizer 1.1.0 - Kombjuudr!
Please see the changelog: https://github.com/nf-core/detaxizer/releases/tag/1.1.0
#deidentification #decontamination #edna #fastq #filter #longreads #metabarcoding #metagenomics #microbiome #nanopore #shortreads #shotgun #taxonomicclassification #taxonomicprofiling #nfcore #openscience #nextflow #bioinformatics
Does #deidentification of #data from #wearable devices give us a false sense of #security? A #SystematicReview
"This systematic review seeks to investigate whether deidentifying data from wearable devices is sufficient to protect the privacy of individuals in datasets. Correct identification rates were typically 86–100%, indicating a high risk of #reidentification. As little as 1–300 s of recording were required to enable reidentification."
https://www.thelancet.com/journals/landig/article/PIIS2589-7500(22)00234-5/fulltext
Wearable devices have made it easier to generate and share data collected on individuals. This systematic review seeks to investigate whether deidentifying data from wearable devices is sufficient to protect the privacy of individuals in datasets. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on Dec 6, 2021 (PROSPERO registration number CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Although our search strategy had no language restrictions, all retrieved studies were in English.
#DeIdentification of #data in #VirtualReality and the #Metaverse is going to be very difficult.
Here's a study showing that 94% of VR users can be identified based on 100 seconds of head and hand motion data, and it's 73% accurate with 10 seconds of data.
With the recent explosive growth of interest and investment in virtual reality (VR) and the so-called "metaverse," public attention has rightly shifted toward the unique security and privacy threats that these platforms may pose. While it has long been known that people reveal information about themselves via their motion, the extent to which this makes an individual globally identifiable within virtual reality has not yet been widely understood. In this study, we show that a large number of real VR users (N=55,541) can be uniquely and reliably identified across multiple sessions using just their head and hand motion relative to virtual objects. After training a classification model on 5 minutes of data per person, a user can be uniquely identified amongst the entire pool of 50,000+ with 94.33% accuracy from 100 seconds of motion, and with 73.20% accuracy from just 10 seconds of motion. This work is the first to truly demonstrate the extent to which biomechanics may serve as a unique identifier in VR, on par with widely used biometrics such as facial or fingerprint recognition.
@jamey I'm personally getting pretty fascinated by the practice of data #deidentification.
I've been trying to better understand the work on #dataleakage by folks like Claudia Perlich (https://sites.google.com/site/claudiaperlich/home)
nf-core
Marco
Christophe Bousquet
Eric Null
was B (this is my old account)
Boing Boing