2,823 research outputs found
Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)
Substance use and abuse is a significant public health problem in the United
States. Group-based intervention programs offer a promising means of preventing
and reducing substance abuse. While effective, unfortunately, inappropriate
intervention groups can result in an increase in deviant behaviors among
participants, a process known as deviancy training. This paper investigates the
problem of optimizing the social influence related to the deviant behavior via
careful construction of the intervention groups. We propose a Mixed Integer
Optimization formulation that decides on the intervention groups, captures the
impact of the groups on the structure of the social network, and models the
impact of these changes on behavior propagation. In addition, we propose a
scalable hybrid meta-heuristic algorithm that combines Mixed Integer
Programming and Large Neighborhood Search to find near-optimal network
partitions. Our algorithm is packaged in the form of GUIDE, an AI-based
decision aid that recommends intervention groups. Being the first quantitative
decision aid of this kind, GUIDE is able to assist practitioners, in particular
social workers, in three key areas: (a) GUIDE proposes near-optimal solutions
that are shown, via extensive simulations, to significantly improve over the
traditional qualitative practices for forming intervention groups; (b) GUIDE is
able to identify circumstances when an intervention will lead to deviancy
training, thus saving time, money, and effort; (c) GUIDE can evaluate current
strategies of group formation and discard strategies that will lead to deviancy
training. In developing GUIDE, we are primarily interested in substance use
interventions among homeless youth as a high risk and vulnerable population.
GUIDE is developed in collaboration with Urban Peak, a homeless-youth serving
organization in Denver, CO, and is under preparation for deployment
Utility Optimal Scheduling and Admission Control for Adaptive Video Streaming in Small Cell Networks
We consider the jointly optimal design of a transmission scheduling and
admission control policy for adaptive video streaming over small cell networks.
We formulate the problem as a dynamic network utility maximization and observe
that it naturally decomposes into two subproblems: admission control and
transmission scheduling. The resulting algorithms are simple and suitable for
distributed implementation. The admission control decisions involve each user
choosing the quality of the video chunk asked for download, based on the
network congestion in its neighborhood. This form of admission control is
compatible with the current video streaming technology based on the DASH
protocol over TCP connections. Through simulations, we evaluate the performance
of the proposed algorithm under realistic assumptions for a small-cell network.Comment: 5 pages, 4 figures. Accepted and will be presented at IEEE
International Symposium on Information Theory (ISIT) 201
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
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