17,648 research outputs found
Formal Computational Unlinkability Proofs of RFID Protocols
We set up a framework for the formal proofs of RFID protocols in the
computational model. We rely on the so-called computationally complete symbolic
attacker model. Our contributions are: i) To design (and prove sound) axioms
reflecting the properties of hash functions (Collision-Resistance, PRF); ii) To
formalize computational unlinkability in the model; iii) To illustrate the
method, providing the first formal proofs of unlinkability of RFID protocols,
in the computational model
Computational Indistinguishability between Quantum States and Its Cryptographic Application
We introduce a computational problem of distinguishing between two specific
quantum states as a new cryptographic problem to design a quantum cryptographic
scheme that is "secure" against any polynomial-time quantum adversary. Our
problem, QSCDff, is to distinguish between two types of random coset states
with a hidden permutation over the symmetric group of finite degree. This
naturally generalizes the commonly-used distinction problem between two
probability distributions in computational cryptography. As our major
contribution, we show that QSCDff has three properties of cryptographic
interest: (i) QSCDff has a trapdoor; (ii) the average-case hardness of QSCDff
coincides with its worst-case hardness; and (iii) QSCDff is computationally at
least as hard as the graph automorphism problem in the worst case. These
cryptographic properties enable us to construct a quantum public-key
cryptosystem, which is likely to withstand any chosen plaintext attack of a
polynomial-time quantum adversary. We further discuss a generalization of
QSCDff, called QSCDcyc, and introduce a multi-bit encryption scheme that relies
on similar cryptographic properties of QSCDcyc.Comment: 24 pages, 2 figures. We improved presentation, and added more detail
proofs and follow-up of recent wor
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball
We present a simple regularization of adversarial perturbations based upon
the perceptual loss. While the resulting perturbations remain imperceptible to
the human eye, they differ from existing adversarial perturbations in that they
are semi-sparse alterations that highlight objects and regions of interest
while leaving the background unaltered. As a semantically meaningful adverse
perturbations, it forms a bridge between counterfactual explanations and
adversarial perturbations in the space of images. We evaluate our approach on
several standard explainability benchmarks, namely, weak localization,
insertion deletion, and the pointing game demonstrating that perceptually
regularized counterfactuals are an effective explanation for image-based
classifiers.Comment: CVPR 202
The Random Oracle Methodology, Revisited
We take a critical look at the relationship between the security of
cryptographic schemes in the Random Oracle Model, and the security of the
schemes that result from implementing the random oracle by so called
"cryptographic hash functions". The main result of this paper is a negative
one: There exist signature and encryption schemes that are secure in the Random
Oracle Model, but for which any implementation of the random oracle results in
insecure schemes.
In the process of devising the above schemes, we consider possible
definitions for the notion of a "good implementation" of a random oracle,
pointing out limitations and challenges.Comment: 31 page
Steganography: a class of secure and robust algorithms
This research work presents a new class of non-blind information hiding
algorithms that are stego-secure and robust. They are based on some finite
domains iterations having the Devaney's topological chaos property. Thanks to a
complete formalization of the approach we prove security against watermark-only
attacks of a large class of steganographic algorithms. Finally a complete study
of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph
CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery
Over the last years, most websites on which users can register (e.g., email
providers and social networks) adopted CAPTCHAs (Completely Automated Public
Turing test to tell Computers and Humans Apart) as a countermeasure against
automated attacks. The battle of wits between designers and attackers of
CAPTCHAs led to current ones being annoying and hard to solve for users, while
still being vulnerable to automated attacks.
In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies
on user interaction. This novel CAPTCHA leverages the innate human ability to
recognize shapes in a confused environment. We assess the effectiveness of our
proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency
to automated attacks. In particular, we evaluated the usability, carrying out a
thorough user study, and we tested the resiliency of our proposal against
several types of automated attacks: traditional ones; designed ad-hoc for our
proposal; and based on machine learning. Compared to the state of the art, our
proposal is more user friendly (e.g., only some 35% of the users prefer current
solutions, such as text-based CAPTCHAs) and more resilient to automated
attacks.Comment: 15 page
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