5,303 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
Prochlo: Strong Privacy for Analytics in the Crowd
The large-scale monitoring of computer users' software activities has become
commonplace, e.g., for application telemetry, error reporting, or demographic
profiling. This paper describes a principled systems architecture---Encode,
Shuffle, Analyze (ESA)---for performing such monitoring with high utility while
also protecting user privacy. The ESA design, and its Prochlo implementation,
are informed by our practical experiences with an existing, large deployment of
privacy-preserving software monitoring.
(cont.; see the paper
Rethinking De-Perimeterisation: Problem Analysis And Solutions
For businesses, the traditional security approach is the hard-shell model: an organisation secures all its assets using a fixed security border, trusting the inside, and distrusting the outside. However, as technologies and business processes change, this model looses its attractiveness. In a networked world, âinsideâ and âoutsideâ can no longer be clearly distinguished. The Jericho Forum - an industry consortium part of the Open Group â coined this process deperimeterisation and suggested an approach aimed at securing data rather than complete systems and infrastructures. We do not question the reality of de-perimeterisation; however, we believe that the existing analysis of the exact problem, as well as the usefulness of the proposed solutions have fallen short: first, there is no linear process of blurring boundaries, in which security mechanisms are placed at lower and lower levels, until they only surround data. To the contrary, we experience a cyclic process of connecting and disconnecting of systems. As conditions change, the basic trade-off between accountability and business opportunities is made (and should be made) every time again. Apart from that, data level security has several limitations to start with, and there is a big potential for solving security problems differently: by rearranging the responsibilities between businesses and individuals. The results of this analysis can be useful for security professionals who need to trade off different security mechanisms for their organisations and their information systems
Privacy, security, and trust issues in smart environments
Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning
K-resolver: Towards Decentralizing Encrypted DNS Resolution
Centralized DNS over HTTPS/TLS (DoH/DoT) resolution, which has started being
deployed by major hosting providers and web browsers, has sparked controversy
among Internet activists and privacy advocates due to several privacy concerns.
This design decision causes the trace of all DNS resolutions to be exposed to a
third-party resolver, different than the one specified by the user's access
network. In this work we propose K-resolver, a DNS resolution mechanism that
disperses DNS queries across multiple DoH resolvers, reducing the amount of
information about a user's browsing activity exposed to each individual
resolver. As a result, none of the resolvers can learn a user's entire web
browsing history. We have implemented a prototype of our approach for Mozilla
Firefox, and used it to evaluate the performance of web page load time compared
to the default centralized DoH approach. While our K-resolver mechanism has
some effect on DNS resolution time and web page load time, we show that this is
mainly due to the geographical location of the selected DoH servers. When more
well-provisioned anycast servers are available, our approach incurs negligible
overhead while improving user privacy.Comment: NDSS Workshop on Measurements, Attacks, and Defenses for the Web
(MADWeb) 202
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