47,413 research outputs found
Q-Class Authentication System for Double Arbiter PUF
Physically Unclonable Function (PUF) is a cryptographic primitive that is based on physical property of each entity or Integrated Circuit (IC) chip. It is expected that PUF be used in security applications such as ID generation and authentication. Some responses from PUF are unreliable, and they are usually discarded. In this paper, we propose a new PUF-based authentication system that exploits information of unreliable responses. In the proposed method, each response is categorized into multiple classes by its unreliability evaluated by feeding the same challenges several times. This authentication system is named Q-class authentication, where Q is the number of classes. We perform experiments assuming a challenge-response authentication system with a certain threshold of errors. Considering 4-class separation for 4-1 Double Arbiter PUF, it is figured out that the advantage of a legitimate prover against a clone is improved form 24% to 36% in terms of success rate. In other words, it is possible to improve the tolerance of machine-learning attack by using unreliable information that was previously regarded disadvantageous to authentication systems
Revealing Relationships among Relevant Climate Variables with Information Theory
A primary objective of the NASA Earth-Sun Exploration Technology Office is to
understand the observed Earth climate variability, thus enabling the
determination and prediction of the climate's response to both natural and
human-induced forcing. We are currently developing a suite of computational
tools that will allow researchers to calculate, from data, a variety of
information-theoretic quantities such as mutual information, which can be used
to identify relationships among climate variables, and transfer entropy, which
indicates the possibility of causal interactions. Our tools estimate these
quantities along with their associated error bars, the latter of which is
critical for describing the degree of uncertainty in the estimates. This work
is based upon optimal binning techniques that we have developed for
piecewise-constant, histogram-style models of the underlying density functions.
Two useful side benefits have already been discovered. The first allows a
researcher to determine whether there exist sufficient data to estimate the
underlying probability density. The second permits one to determine an
acceptable degree of round-off when compressing data for efficient transfer and
storage. We also demonstrate how mutual information and transfer entropy can be
applied so as to allow researchers not only to identify relations among climate
variables, but also to characterize and quantify their possible causal
interactions.Comment: 14 pages, 5 figures, Proceedings of the Earth-Sun System Technology
Conference (ESTC 2005), Adelphi, M
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