https://www.spectrumnews.org/news/sanitizing-functional-genomics-data-may-prevent-privacy-breaches/
Posts Tagged ‘privacy’
‘Sanitizing’ functional genomics data may prevent privacy breaches | Spectrum | Autism Research News
January 8, 2021BBC Media Request = Data Sanitisation
December 22, 2020The Naked Scientists
Data sanitisation tool plugs privacy gap
Functional genomics data is vulnerable to de-anonymising attacks… 17 December 2020
Interview with
Mark Gerstein, Yale University
Part of the show RNA Vaccines, Privacy, and Penguins
part of the Naked Genetics podcast, on all podcast platforms and o
site here: https://www.thenakedscientists.com/podcasts/naked-genetics/rna-vaccines-privacy-and-penguins
direct link: https://www.thenakedscientists.com/articles/interviews/data-sanitisation-tool-plugs-privacy-gap
Dressing for the Surveillance Age | The New Yorker
December 13, 2020https://www.newyorker.com/magazine/2020/03/16/dressing-for-the-surveillance-age
Much of this @jmseabrook article about facial recognition will soon be applicable to genomic privacy & individuals’ attempts to protect themselves in this sphere as well…
QT:{{”
adversarialfashion.com
…
Adversarial examples demonstrate that deep-learning-based C.V. systems are only as good as their training data, and, because the data sets don’t contain all possible images, we can’t really trust them. In spite of the gains in accuracy and performance since the switch to deep learning, we still don’t understand or control how C.V. systems make decisions. “You train a neural network on inputs that represent the world a certain way,” Goldstein said. “And maybe something comes along that’s different—a lighting condition the system didn’t expect, or clothing it didn’t expect. It’s important that these systems are robust and don’t fail catastrophically when they stumble on something they aren’t trained on.”
The early work on adversarial attacks was done in the digital realm, using two-dimensional computer-generated images in a simulation. Making a three-dimensional adversarial object that could work in the real world is a lot harder, because shadows and partial views defeat the attack by introducing nuisance variables into the input image. A Belgian team of researchers printed adversarial images on
two-dimensional boards, which made them invisible to yolo when they held the boards in front of them. Scientists at Northeastern University and at the M.I.T.-I.B.M. Watson A.I. Lab created an adversarial design that they printed on a T-shirt. Goldstein and his students came up with a whole line of clothes—hoodies, sweatshirts, T-shirts.
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Tests Show Genetic Signature of Coronavirus That Likely Infected Trump – The New York Times
November 7, 2020When you browse Instagram and find former Australian Prime Minister Tony Abbott’s passport number
October 5, 2020this is quite interesting privacy leak
GDPR’s effect on genomic research outside the Europe
August 9, 2020from the NIH session at ISMB
July 27, 2020privacy component in the first year from nhgri – this initiative is similar to bd2k
https://dpcpsi.nih.gov/sites/default/files/CoC_May_2020_1.05PM_Concept_Clearance_AIBLE_Brennan_508.pdf
Barcoded microbial system for high-resolution object provenance | Science
June 27, 2020https://science.sciencemag.org/content/368/6495/1135
Interesting paper. Wonder whether this technology could readily track people and thus has #privacy implications…
Barcoded microbial system for high-resolution object provenance
Jason Qian1,2,3,*, Zhi-xiang Lu1,2,*, Christopher P. Mancuso4,*, Han-Ying Jhuang1,*, Rocío del Carmen Barajas-Ornelas5,*, Sarah A. Boswell1,2,*, Fernando H. Ramírez-Guadiana5, Victoria Jones1,6,†, Akhila Sonti4, Kole Sedlack4,‡, Lior Artzi5, Giyoung Jung7, Mohammad Arammash1, Mary E. Pettit1, Michael Melfi1, Lorena Lyon1, Siân V. Owen6, Michael Baym2,6, Ahmad S. Khalil4,8, Pamela A. Silver1,8, David Z. Rudner5, Michael Springer1,2,§
Science 05 Jun 2020:
Vol. 368, Issue 6495, pp. 1135-1140
DOI: 10.1126/science.aba5584
