96,393 research outputs found
Faster quantum mixing for slowly evolving sequences of Markov chains
Markov chain methods are remarkably successful in computational physics,
machine learning, and combinatorial optimization. The cost of such methods
often reduces to the mixing time, i.e., the time required to reach the steady
state of the Markov chain, which scales as , the inverse of the
spectral gap. It has long been conjectured that quantum computers offer nearly
generic quadratic improvements for mixing problems. However, except in special
cases, quantum algorithms achieve a run-time of , which introduces a costly dependence on the Markov chain size
not present in the classical case. Here, we re-address the problem of mixing of
Markov chains when these form a slowly evolving sequence. This setting is akin
to the simulated annealing setting and is commonly encountered in physics,
material sciences and machine learning. We provide a quantum memory-efficient
algorithm with a run-time of ,
neglecting logarithmic terms, which is an important improvement for large state
spaces. Moreover, our algorithms output quantum encodings of distributions,
which has advantages over classical outputs. Finally, we discuss the run-time
bounds of mixing algorithms and show that, under certain assumptions, our
algorithms are optimal.Comment: 20 pages, 2 figure
Covert Ephemeral Communication in Named Data Networking
In the last decade, there has been a growing realization that the current
Internet Protocol is reaching the limits of its senescence. This has prompted
several research efforts that aim to design potential next-generation Internet
architectures. Named Data Networking (NDN), an instantiation of the
content-centric approach to networking, is one such effort. In contrast with
IP, NDN routers maintain a significant amount of user-driven state. In this
paper we investigate how to use this state for covert ephemeral communication
(CEC). CEC allows two or more parties to covertly exchange ephemeral messages,
i.e., messages that become unavailable after a certain amount of time. Our
techniques rely only on network-layer, rather than application-layer, services.
This makes our protocols robust, and communication difficult to uncover. We
show that users can build high-bandwidth CECs exploiting features unique to
NDN: in-network caches, routers' forwarding state and name matching rules. We
assess feasibility and performance of proposed cover channels using a local
setup and the official NDN testbed
Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data Networking
Content-Centric Networking (CCN) is an emerging networking paradigm being
considered as a possible replacement for the current IP-based host-centric
Internet infrastructure. In CCN, named content becomes a first-class entity.
CCN focuses on content distribution, which dominates current Internet traffic
and is arguably not well served by IP. Named-Data Networking (NDN) is an
example of CCN. NDN is also an active research project under the NSF Future
Internet Architectures (FIA) program. FIA emphasizes security and privacy from
the outset and by design. To be a viable Internet architecture, NDN must be
resilient against current and emerging threats. This paper focuses on
distributed denial-of-service (DDoS) attacks; in particular we address interest
flooding, an attack that exploits key architectural features of NDN. We show
that an adversary with limited resources can implement such attack, having a
significant impact on network performance. We then introduce Poseidon: a
framework for detecting and mitigating interest flooding attacks. Finally, we
report on results of extensive simulations assessing proposed countermeasure.Comment: The IEEE Conference on Local Computer Networks (LCN 2013
Spartan Daily, September 20, 1977
Volume 69, Issue 11https://scholarworks.sjsu.edu/spartandaily/6234/thumbnail.jp
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