641,680 research outputs found
A Game-Theoretic View of the Interference Channel: Impact of Coordination and Bargaining
This work considers coordination and bargaining between two selfish users
over a Gaussian interference channel. The usual information theoretic approach
assumes full cooperation among users for codebook and rate selection. In the
scenario investigated here, each user is willing to coordinate its actions only
when an incentive exists and benefits of cooperation are fairly allocated. The
users are first allowed to negotiate for the use of a simple Han-Kobayashi type
scheme with fixed power split. Conditions for which users have incentives to
cooperate are identified. Then, two different approaches are used to solve the
associated bargaining problem. First, the Nash Bargaining Solution (NBS) is
used as a tool to get fair information rates and the operating point is
obtained as a result of an optimization problem. Next, a dynamic
alternating-offer bargaining game (AOBG) from bargaining theory is introduced
to model the bargaining process and the rates resulting from negotiation are
characterized. The relationship between the NBS and the equilibrium outcome of
the AOBG is studied and factors that may affect the bargaining outcome are
discussed. Finally, under certain high signal-to-noise ratio regimes, the
bargaining problem for the generalized degrees of freedom is studied.Comment: 43 pages, 11 figures, to appear on Special Issue of the IEEE
Transactions on Information Theory on Interference Networks, 201
Deterministic Symmetry Breaking in Ring Networks
We study a distributed coordination mechanism for uniform agents located on a
circle. The agents perform their actions in synchronised rounds. At the
beginning of each round an agent chooses the direction of its movement from
clockwise, anticlockwise, or idle, and moves at unit speed during this round.
Agents are not allowed to overpass, i.e., when an agent collides with another
it instantly starts moving with the same speed in the opposite direction
(without exchanging any information with the other agent). However, at the end
of each round each agent has access to limited information regarding its
trajectory of movement during this round.
We assume that mobile agents are initially located on a circle unit
circumference at arbitrary but distinct positions unknown to other agents. The
agents are equipped with unique identifiers from a fixed range. The {\em
location discovery} task to be performed by each agent is to determine the
initial position of every other agent.
Our main result states that, if the only available information about movement
in a round is limited to %information about distance between the initial and
the final position, then there is a superlinear lower bound on time needed to
solve the location discovery problem. Interestingly, this result corresponds to
a combinatorial symmetry breaking problem, which might be of independent
interest. If, on the other hand, an agent has access to the distance to its
first collision with another agent in a round, we design an asymptotically
efficient and close to optimal solution for the location discovery problem.Comment: Conference version accepted to ICDCS 201
Alternating-Offer Bargaining Games over the Gaussian Interference Channel
This paper tackles the problem of how two selfish users jointly determine the
operating point in the achievable rate region of a two-user Gaussian
interference channel through bargaining. In previous work, incentive conditions
for two users to cooperate using a simple version of Han-Kobayashi scheme was
studied and the Nash bargaining solution (NBS) was used to obtain a fair
operating point. Here a noncooperative bargaining game of alternating offers is
adopted to model the bargaining process and rates resulting from the
equilibrium outcome are analyzed. In particular, it is shown that the operating
point resulting from the formulated bargaining game depends on the cost of
delay in bargaining and how bargaining proceeds. If the associated bargaining
problem is regular, a unique perfect equilibrium exists and lies on the
individual rational efficient frontier of the achievable rate region. Besides,
the equilibrium outcome approaches the NBS if the bargaining costs of both
users are negligible.Comment: 8 pages, 6 figures, to appear in Proceedings of Forty-Eighth Annual
Allerton Conference on Communication, Control, and Computin
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
Complexity of Multi-Value Byzantine Agreement
In this paper, we consider the problem of maximizing the throughput of
Byzantine agreement, given that the sum capacity of all links in between nodes
in the system is finite. We have proposed a highly efficient Byzantine
agreement algorithm on values of length l>1 bits. This algorithm uses error
detecting network codes to ensure that fault-free nodes will never disagree,
and routing scheme that is adaptive to the result of error detection. Our
algorithm has a bit complexity of n(n-1)l/(n-t), which leads to a linear cost
(O(n)) per bit agreed upon, and overcomes the quadratic lower bound
(Omega(n^2)) in the literature. Such linear per bit complexity has only been
achieved in the literature by allowing a positive probability of error. Our
algorithm achieves the linear per bit complexity while guaranteeing agreement
is achieved correctly even in the worst case. We also conjecture that our
algorithm can be used to achieve agreement throughput arbitrarily close to the
agreement capacity of a network, when the sum capacity is given
On the Simulatability Condition in Key Generation Over a Non-authenticated Public Channel
Simulatability condition is a fundamental concept in studying key generation
over a non-authenticated public channel, in which Eve is active and can
intercept, modify and falsify messages exchanged over the non-authenticated
public channel. Using this condition, Maurer and Wolf showed a remarkable "all
or nothing" result: if the simulatability condition does not hold, the key
capacity over the non-authenticated public channel will be the same as that of
the case with a passive Eve, while the key capacity over the non-authenticated
channel will be zero if the simulatability condition holds. However, two
questions remain open so far: 1) For a given joint probability mass function
(PMF), are there efficient algorithms (polynomial complexity algorithms) for
checking whether the simulatability condition holds or not?; and 2) If the
simulatability condition holds, are there efficient algorithms for finding the
corresponding attack strategy? In this paper, we answer these two open
questions affirmatively. In particular, for a given joint PMF, we construct a
linear programming (LP) problem and show that the simulatability condition
holds \textit{if and only if} the optimal value obtained from the constructed
LP is zero. Furthermore, we construct another LP and show that the minimizer of
the newly constructed LP is a valid attack strategy. Both LPs can be solved
with a polynomial complexity
Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state of a
discrete Markov random field (MRF), also known as the MRF energy minimization
problem. The task is known to be NP-hard in general and its practical
importance motivates numerous approximate algorithms. We propose a submodular
relaxation approach (SMR) based on a Lagrangian relaxation of the initial
problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR
does not decompose the graph structure of the initial problem but constructs a
submodular energy that is minimized within the Lagrangian relaxation. Our
approach is applicable to both pairwise and high-order MRFs and allows to take
into account global potentials of certain types. We study theoretical
properties of the proposed approach and evaluate it experimentally.Comment: This paper is accepted for publication in IEEE Transactions on
Pattern Analysis and Machine Intelligenc
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