587 research outputs found
Participation Cost Estimation: Private Versus Non-Private Study
In our study, we seek to learn the real-time crowd levels at popular points
of interests based on users continually sharing their location data. We
evaluate the benefits of users sharing their location data privately and
non-privately, and show that suitable privacy-preserving mechanisms provide
incentives for user participation in a private study as compared to a
non-private study
WARP: A ICN architecture for social data
Social network companies maintain complete visibility and ownership of the
data they store. However users should be able to maintain full control over
their content. For this purpose, we propose WARP, an architecture based upon
Information-Centric Networking (ICN) designs, which expands the scope of the
ICN architecture beyond media distribution, to provide data control in social
networks. The benefit of our solution lies in the lightweight nature of the
protocol and in its layered design. With WARP, data distribution and access
policies are enforced on the user side. Data can still be replicated in an ICN
fashion but we introduce control channels, named \textit{thread updates}, which
ensures that the access to the data is always updated to the latest control
policy. WARP decentralizes the social network but still offers APIs so that
social network providers can build products and business models on top of WARP.
Social applications run directly on the user's device and store their data on
the user's \textit{butler} that takes care of encryption and distribution.
Moreover, users can still rely on third parties to have high-availability
without renouncing their privacy
XYZ Privacy
Future autonomous vehicles will generate, collect, aggregate and consume
significant volumes of data as key gateway devices in emerging Internet of
Things scenarios. While vehicles are widely accepted as one of the most
challenging mobility contexts in which to achieve effective data
communications, less attention has been paid to the privacy of data emerging
from these vehicles. The quality and usability of such privatized data will lie
at the heart of future safe and efficient transportation solutions.
In this paper, we present the XYZ Privacy mechanism. XYZ Privacy is to our
knowledge the first such mechanism that enables data creators to submit
multiple contradictory responses to a query, whilst preserving utility measured
as the absolute error from the actual original data. The functionalities are
achieved in both a scalable and secure fashion. For instance, individual
location data can be obfuscated while preserving utility, thereby enabling the
scheme to transparently integrate with existing systems (e.g. Waze). A new
cryptographic primitive Function Secret Sharing is used to achieve
non-attributable writes and we show an order of magnitude improvement from the
default implementation.Comment: arXiv admin note: text overlap with arXiv:1708.0188
Design and analysis of fault-tolerant multibus interconnection networks
AbstractIn this paper a new class of fault-tolerant multibus interconnection networks is presented and analyzed. Efficiency and fault tolerance have been the driving forces in the design of these structures. The most common types of faults have been explicitly considered and in particular the jabbering problem has been adequately resolved. The analysis covers the evaluation of capacity, throughput and average delay and it includes faults of one or more channels. The system is shown to be very efficient and to be able to adequately support channel and station faults
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