2,963 research outputs found
Cryptanalysis of a multi-party quantum key agreement protocol with single particles
Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x]
presented an efficient multi-party quantum key agreement (QKA) protocol by
employing single particles and unitary operations. The aim of this protocol is
to fairly and securely negotiate a secret session key among parties with a
high qubit efficiency. In addition, the authors claimed that no participant can
learn anything more than his/her prescribed output in this protocol, i.e., the
sub-secret keys of the participants can be kept secret during the protocol.
However, here we points out that the sub-secret of a participant in Sun et
al.'s protocol can be eavesdropped by the two participants next to him/her. In
addition, a certain number of dishonest participants can fully determine the
final shared key in this protocol. Finally, we discuss the factors that should
be considered when designing a really fair and secure QKA protocol.Comment: 7 page
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
The proximal gradient algorithm has been popularly used for convex
optimization. Recently, it has also been extended for nonconvex problems, and
the current state-of-the-art is the nonmonotone accelerated proximal gradient
algorithm. However, it typically requires two exact proximal steps in each
iteration, and can be inefficient when the proximal step is expensive. In this
paper, we propose an efficient proximal gradient algorithm that requires only
one inexact (and thus less expensive) proximal step in each iteration.
Convergence to a critical point %of the nonconvex problem is still guaranteed
and has a convergence rate, which is the best rate for nonconvex
problems with first-order methods. Experiments on a number of problems
demonstrate that the proposed algorithm has comparable performance as the
state-of-the-art, but is much faster
Agent Behavior Prediction and Its Generalization Analysis
Machine learning algorithms have been applied to predict agent behaviors in
real-world dynamic systems, such as advertiser behaviors in sponsored search
and worker behaviors in crowdsourcing. The behavior data in these systems are
generated by live agents: once the systems change due to the adoption of the
prediction models learnt from the behavior data, agents will observe and
respond to these changes by changing their own behaviors accordingly. As a
result, the behavior data will evolve and will not be identically and
independently distributed, posing great challenges to the theoretical analysis
on the machine learning algorithms for behavior prediction. To tackle this
challenge, in this paper, we propose to use Markov Chain in Random Environments
(MCRE) to describe the behavior data, and perform generalization analysis of
the machine learning algorithms on its basis. Since the one-step transition
probability matrix of MCRE depends on both previous states and the random
environment, conventional techniques for generalization analysis cannot be
directly applied. To address this issue, we propose a novel technique that
transforms the original MCRE into a higher-dimensional time-homogeneous Markov
chain. The new Markov chain involves more variables but is more regular, and
thus easier to deal with. We prove the convergence of the new Markov chain when
time approaches infinity. Then we prove a generalization bound for the machine
learning algorithms on the behavior data generated by the new Markov chain,
which depends on both the Markovian parameters and the covering number of the
function class compounded by the loss function for behavior prediction and the
behavior prediction model. To the best of our knowledge, this is the first work
that performs the generalization analysis on data generated by complex
processes in real-world dynamic systems
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