21,521 research outputs found
From Social Data Mining to Forecasting Socio-Economic Crisis
Socio-economic data mining has a great potential in terms of gaining a better
understanding of problems that our economy and society are facing, such as
financial instability, shortages of resources, or conflicts. Without
large-scale data mining, progress in these areas seems hard or impossible.
Therefore, a suitable, distributed data mining infrastructure and research
centers should be built in Europe. It also appears appropriate to build a
network of Crisis Observatories. They can be imagined as laboratories devoted
to the gathering and processing of enormous volumes of data on both natural
systems such as the Earth and its ecosystem, as well as on human
techno-socio-economic systems, so as to gain early warnings of impending
events. Reality mining provides the chance to adapt more quickly and more
accurately to changing situations. Further opportunities arise by individually
customized services, which however should be provided in a privacy-respecting
way. This requires the development of novel ICT (such as a self- organizing
Web), but most likely new legal regulations and suitable institutions as well.
As long as such regulations are lacking on a world-wide scale, it is in the
public interest that scientists explore what can be done with the huge data
available. Big data do have the potential to change or even threaten democratic
societies. The same applies to sudden and large-scale failures of ICT systems.
Therefore, dealing with data must be done with a large degree of responsibility
and care. Self-interests of individuals, companies or institutions have limits,
where the public interest is affected, and public interest is not a sufficient
justification to violate human rights of individuals. Privacy is a high good,
as confidentiality is, and damaging it would have serious side effects for
society.Comment: 65 pages, 1 figure, Visioneer White Paper, see
http://www.visioneer.ethz.c
The Social Cost of Cheap Pseudonyms
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71559/1/j.1430-9134.2001.00173.x.pd
Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses
The gradual fusion of intelligent transportation systems with metaverse
technologies is giving rise to vehicular metaverses, which blend virtual spaces
with physical space. As indispensable components for vehicular metaverses,
Vehicular Twins (VTs) are digital replicas of Vehicular Metaverse Users (VMUs)
and facilitate customized metaverse services to VMUs. VTs are established and
maintained in RoadSide Units (RSUs) with sufficient computing and storage
resources. Due to the limited communication coverage of RSUs and the high
mobility of VMUs, VTs need to be migrated among RSUs to ensure real-time and
seamless services for VMUs. However, during VT migrations, physical-virtual
synchronization and massive communications among VTs may cause identity and
location privacy disclosures of VMUs and VTs. In this article, we study privacy
issues and the corresponding defenses for VT migrations in vehicular
metaverses. We first present four kinds of specific privacy attacks during VT
migrations. Then, we propose a VMU-VT dual pseudonym scheme and a synchronous
pseudonym change framework to defend against these attacks. Additionally, we
evaluate average privacy entropy for pseudonym changes and optimize the number
of pseudonym distribution based on inventory theory. Numerical results show
that the average utility of VMUs under our proposed schemes is 33.8% higher
than that under the equal distribution scheme, demonstrating the superiority of
our schemes.Comment: 8 pages, 6 figure
Hang With Your Buddies to Resist Intersection Attacks
Some anonymity schemes might in principle protect users from pervasive
network surveillance - but only if all messages are independent and unlinkable.
Users in practice often need pseudonymity - sending messages intentionally
linkable to each other but not to the sender - but pseudonymity in dynamic
networks exposes users to intersection attacks. We present Buddies, the first
systematic design for intersection attack resistance in practical anonymity
systems. Buddies groups users dynamically into buddy sets, controlling message
transmission to make buddies within a set behaviorally indistinguishable under
traffic analysis. To manage the inevitable tradeoffs between anonymity
guarantees and communication responsiveness, Buddies enables users to select
independent attack mitigation policies for each pseudonym. Using trace-based
simulations and a working prototype, we find that Buddies can guarantee
non-trivial anonymity set sizes in realistic chat/microblogging scenarios, for
both short-lived and long-lived pseudonyms.Comment: 15 pages, 8 figure
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