106,485 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
Invisible Pixels Are Dead, Long Live Invisible Pixels!
Privacy has deteriorated in the world wide web ever since the 1990s. The
tracking of browsing habits by different third-parties has been at the center
of this deterioration. Web cookies and so-called web beacons have been the
classical ways to implement third-party tracking. Due to the introduction of
more sophisticated technical tracking solutions and other fundamental
transformations, the use of classical image-based web beacons might be expected
to have lost their appeal. According to a sample of over thirty thousand images
collected from popular websites, this paper shows that such an assumption is a
fallacy: classical 1 x 1 images are still commonly used for third-party
tracking in the contemporary world wide web. While it seems that ad-blockers
are unable to fully block these classical image-based tracking beacons, the
paper further demonstrates that even limited information can be used to
accurately classify the third-party 1 x 1 images from other images. An average
classification accuracy of 0.956 is reached in the empirical experiment. With
these results the paper contributes to the ongoing attempts to better
understand the lack of privacy in the world wide web, and the means by which
the situation might be eventually improved.Comment: Forthcoming in the 17th Workshop on Privacy in the Electronic Society
(WPES 2018), Toronto, AC
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