193 research outputs found
Statistically-secure ORAM with Overhead
We demonstrate a simple, statistically secure, ORAM with computational
overhead ; previous ORAM protocols achieve only
computational security (under computational assumptions) or require
overheard. An additional benefit of our ORAM is its
conceptual simplicity, which makes it easy to implement in both software and
(commercially available) hardware.
Our construction is based on recent ORAM constructions due to Shi, Chan,
Stefanov, and Li (Asiacrypt 2011) and Stefanov and Shi (ArXiv 2012), but with
some crucial modifications in the algorithm that simplifies the ORAM and enable
our analysis. A central component in our analysis is reducing the analysis of
our algorithm to a "supermarket" problem; of independent interest (and of
importance to our analysis,) we provide an upper bound on the rate of "upset"
customers in the "supermarket" problem
A Methodology for Analyzing Power Consumption in Wireless Communication Systems
Energy usage has become an important issue in wireless communication systems. The energy-intensive nature of wireless communication has spurred concern over how best systems can make the most use of this non-renewable resource. Research in energy-efficient design of wireless communication systems show that one of its challenges is that the overall performance of the system depends, in a coupled way, on the different submodules of the system i.e. antenna, power amplifier, modulation, error control coding, and network architecture. Network architecture implementation strategies offer protocol software implementors an opportunity of incorporating low-power strategies into the design of the network protocols used for data communication.
This dissertation proposes a methodology that would allow a software protocol implementor to analyze the power consumption of a wireless communication system. The foundation of this methodology lies in the understanding of the formal specification of the wireless interface network architecture which can be used to predict the performance of the system. By extending this hypothesis, a protocol implementor can use the formal specification to derive the power consumption behaviour of the wireless system during a normal operation (transmission or reception of data). A high-level formalism like state-transition graphs, can be used to track the protocol processing behaviour and to derive the associated continuous-time Markov chains.
Because of their diversity, Markov reward models(MRM) are used to model the power consumption associated with the different states of a specified protocol layer. The models are solved analytically using the Mobius performance and dependability tool. Using the MRM accumulation and utilization measures, a profile of the power consumption is generated. Results from the experiments on the protocol layers show the individual power consumption and utilization of the different states as well as the accumulated power consumption of different protocol layers when compared. Ultimately, the results from the reward model solution can be used in the energy-efficient design of wireless communication systems.
Lastly, in order to get an idea of how wireless communication device companies handle issues of power consumption, we consulted with the wireless module engineers at Siemens Communication South Africa and present our findings on current practices in energy efficient protocol implementation
List of requirements on formalisms and selection of appropriate tools
This deliverable reports on the activities for the set-up of the modelling environments for the evaluation activities of WP5. To this objective, it reports on the identified modelling peculiarities of the electric power infrastructure and the information infrastructures and of their interdependencies, recalls the tools that have been considered and concentrates on the tools that are, and will be, used in the project: DrawNET, DEEM and EPSys which have been developed before and during the project by the partners, and M\uf6bius and PRISM, developed respectively at the University of Illinois at Urbana Champaign and at the University of Birmingham (and recently at the University of Oxford)
Enhancing PGA Tour Performance: Leveraging ShotlinkTM Data for Optimization and Prediction
In this study, we demonstrate how data from the PGA Tour, combined with
stochastic shortest path models (MDPs), can be employed to refine the
strategies of professional golfers and predict future performances. We present
a comprehensive methodology for this objective, proving its computational
feasibility. This sets the stage for more in-depth exploration into leveraging
data available to professional and amateurs for strategic optimization and
forecasting performance in golf. For the replicability of our results, and to
adapt and extend the methodology and prototype solution, we provide access to
all our codes and analyses (R and C++)
Parallel Global Edge Switching for the Uniform Sampling of Simple Graphs with Prescribed Degrees
The uniform sampling of simple graphs matching a prescribed degree sequence
is an important tool in network science, e.g. to construct graph generators or
null-models. Here, the Edge Switching Markov Chain (ES-MC) is a common choice.
Given an arbitrary simple graph with the required degree sequence, ES-MC
carries out a large number of small changes, called edge switches, to
eventually obtain a uniform sample. In practice, reasonably short runs
efficiently yield approximate uniform samples.
In this work, we study the problem of executing edge switches in parallel. We
discuss parallelizations of ES-MC, but find that this approach suffers from
complex dependencies between edge switches. For this reason, we propose the
Global Edge Switching Markov Chain (G-ES-MC), an ES-MC variant with simpler
dependencies. We show that G-ES-MC converges to the uniform distribution and
design shared-memory parallel algorithms for ES-MC and G-ES-MC. In an empirical
evaluation, we provide evidence that G-ES-MC requires not more switches than
ES-MC (and often fewer), and demonstrate the efficiency and scalability of our
parallel G-ES-MC implementation
Generation and properties of random graphs and analysis of randomized algorithms
We study a new method of generating random -regular graphs by
repeatedly applying an operation called pegging. The pegging
algorithm, which applies the pegging operation in each step, is a
method of generating large random regular graphs beginning with
small ones. We prove that the limiting joint distribution of the
numbers of short cycles in the resulting graph is independent
Poisson. We use the coupling method to bound the total variation
distance between the joint distribution of short cycle counts and
its limit and thereby show that is an upper bound
of the \eps-mixing time. The coupling involves two different,
though quite similar, Markov chains that are not time-homogeneous.
We also show that the -mixing time is not
. This demonstrates that the upper bound
is essentially tight. We study also the
connectivity of random -regular graphs generated by the pegging
algorithm. We show that these graphs are asymptotically almost
surely -connected for any even constant .
The problem of orientation of random hypergraphs is motivated by the
classical load balancing problem. Let be two fixed integers.
Let \orH be a hypergraph whose hyperedges are uniformly of size
.
To {\em -orient} a hyperedge, we assign exactly of its
vertices positive signs with respect to this hyperedge, and the rest
negative. A -orientation of \orH consists of a
-orientation of all hyperedges of \orH, such that each vertex
receives at most positive signs from its incident hyperedges.
When is large enough, we determine the threshold of the
existence of a -orientation of a random hypergraph. The
-orientation of hypergraphs is strongly related to a general
version of the off-line load balancing problem.
The other topic we discuss is computing the probability of induced
subgraphs in a random regular graph. Let and be a graph
on vertices. For any with , we compute the
probability that the subgraph of induced by
is . The result holds for any and is further
extended to , the probability space of
random graphs with given degree sequence . This result
provides a basic tool for studying properties, for instance the
existence or the counts, of certain types of induced subgraphs
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