20,850 research outputs found
PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies
Many interactions between network users rely on trust, which is becoming
particularly important given the security breaches in the Internet today. These
problems are further exacerbated by the dynamics in wireless mobile networks.
In this paper we address the issue of trust advisory and establishment in
mobile networks, with application to ad hoc networks, including DTNs. We
utilize encounters in mobile societies in novel ways, noticing that mobility
provides opportunities to build proximity, location and similarity based trust.
Four new trust advisor filters are introduced - including encounter frequency,
duration, behavior vectors and behavior matrices - and evaluated over an
extensive set of real-world traces collected from a major university. Two sets
of statistical analyses are performed; the first examines the underlying
encounter relationships in mobile societies, and the second evaluates DTN
routing in mobile peer-to-peer networks using trust and selfishness models. We
find that for the analyzed trace, trust filters are stable in terms of growth
with time (3 filters have close to 90% overlap of users over a period of 9
weeks) and the results produced by different filters are noticeably different.
In our analysis for trust and selfishness model, our trust filters largely undo
the effect of selfishness on the unreachability in a network. Thus improving
the connectivity in a network with selfish nodes.
We hope that our initial promising results open the door for further research
on proximity-based trust
Dynamics, robustness and fragility of trust
Trust is often conveyed through delegation, or through recommendation. This
makes the trust authorities, who process and publish trust recommendations,
into an attractive target for attacks and spoofing. In some recent empiric
studies, this was shown to lead to a remarkable phenomenon of *adverse
selection*: a greater percentage of unreliable or malicious web merchants were
found among those with certain types of trust certificates, then among those
without. While such findings can be attributed to a lack of diligence in trust
authorities, or even to conflicts of interest, our analysis of trust dynamics
suggests that public trust networks would probably remain vulnerable even if
trust authorities were perfectly diligent. The reason is that the process of
trust building, if trust is not breached too often, naturally leads to
power-law distributions: the rich get richer, the trusted attract more trust.
The evolutionary processes with such distributions, ubiquitous in nature, are
known to be robust with respect to random failures, but vulnerable to adaptive
attacks. We recommend some ways to decrease the vulnerability of trust
building, and suggest some ideas for exploration.Comment: 17 pages; simplified the statement and the proof of the main theorem;
FAST 200
Embedding Graphs under Centrality Constraints for Network Visualization
Visual rendering of graphs is a key task in the mapping of complex network
data. Although most graph drawing algorithms emphasize aesthetic appeal,
certain applications such as travel-time maps place more importance on
visualization of structural network properties. The present paper advocates two
graph embedding approaches with centrality considerations to comply with node
hierarchy. The problem is formulated first as one of constrained
multi-dimensional scaling (MDS), and it is solved via block coordinate descent
iterations with successive approximations and guaranteed convergence to a KKT
point. In addition, a regularization term enforcing graph smoothness is
incorporated with the goal of reducing edge crossings. A second approach
leverages the locally-linear embedding (LLE) algorithm which assumes that the
graph encodes data sampled from a low-dimensional manifold. Closed-form
solutions to the resulting centrality-constrained optimization problems are
determined yielding meaningful embeddings. Experimental results demonstrate the
efficacy of both approaches, especially for visualizing large networks on the
order of thousands of nodes.Comment: Submitted to IEEE Transactions on Visualization and Computer Graphic
Modeling Complex Networks For (Electronic) Commerce
NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
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