23,038 research outputs found
Assessing statistical significance of periodogram peaks
The least-squares (or Lomb-Scargle) periodogram is a powerful tool which is
used routinely in many branches of astronomy to search for periodicities in
observational data. The problem of assessing statistical significance of
candidate periodicities for different periodograms is considered. Based on
results in extreme value theory, improved analytic estimations of false alarm
probabilities are given. They include an upper limit to the false alarm
probability (or a lower limit to the significance). These estimations are
tested numerically in order to establish regions of their practical
applicability.Comment: 7 pages, 6 figures, 1 table; To be published in MNRA
Secure and Efficient RNS Approach for Elliptic Curve Cryptography
Scalar multiplication, the main operation in elliptic
curve cryptographic protocols, is vulnerable to side-channel
(SCA) and fault injection (FA) attacks. An efficient countermeasure
for scalar multiplication can be provided by using alternative
number systems like the Residue Number System (RNS). In RNS,
a number is represented as a set of smaller numbers, where each
one is the result of the modular reduction with a given moduli
basis. Under certain requirements, a number can be uniquely
transformed from the integers to the RNS domain (and vice
versa) and all arithmetic operations can be performed in RNS.
This representation provides an inherent SCA and FA resistance
to many attacks and can be further enhanced by RNS arithmetic
manipulation or more traditional algorithmic countermeasures.
In this paper, extending our previous work, we explore the
potentials of RNS as an SCA and FA countermeasure and provide
an description of RNS based SCA and FA resistance means. We
propose a secure and efficient Montgomery Power Ladder based
scalar multiplication algorithm on RNS and discuss its SCAFA
resistance. The proposed algorithm is implemented on an
ARM Cortex A7 processor and its SCA-FA resistance is evaluated
by collecting preliminary leakage trace results that validate our
initial assumptions
On the Duality of Probing and Fault Attacks
In this work we investigate the problem of simultaneous privacy and integrity
protection in cryptographic circuits. We consider a white-box scenario with a
powerful, yet limited attacker. A concise metric for the level of probing and
fault security is introduced, which is directly related to the capabilities of
a realistic attacker. In order to investigate the interrelation of probing and
fault security we introduce a common mathematical framework based on the
formalism of information and coding theory. The framework unifies the known
linear masking schemes. We proof a central theorem about the properties of
linear codes which leads to optimal secret sharing schemes. These schemes
provide the lower bound for the number of masks needed to counteract an
attacker with a given strength. The new formalism reveals an intriguing duality
principle between the problems of probing and fault security, and provides a
unified view on privacy and integrity protection using error detecting codes.
Finally, we introduce a new class of linear tamper-resistant codes. These are
eligible to preserve security against an attacker mounting simultaneous probing
and fault attacks
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Machine Learning (ML) algorithms are used to train computers to perform a
variety of complex tasks and improve with experience. Computers learn how to
recognize patterns, make unintended decisions, or react to a dynamic
environment. Certain trained machines may be more effective than others because
they are based on more suitable ML algorithms or because they were trained
through superior training sets. Although ML algorithms are known and publicly
released, training sets may not be reasonably ascertainable and, indeed, may be
guarded as trade secrets. While much research has been performed about the
privacy of the elements of training sets, in this paper we focus our attention
on ML classifiers and on the statistical information that can be unconsciously
or maliciously revealed from them. We show that it is possible to infer
unexpected but useful information from ML classifiers. In particular, we build
a novel meta-classifier and train it to hack other classifiers, obtaining
meaningful information about their training sets. This kind of information
leakage can be exploited, for example, by a vendor to build more effective
classifiers or to simply acquire trade secrets from a competitor's apparatus,
potentially violating its intellectual property rights
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