10,638 research outputs found
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
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
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