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The Computer Journal
Article . 2005 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 2023
Data sources: DBLP
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Probabilistic Visual Cryptography Schemes

Authors: Stelvio Cimato; Roberto De Prisco; Alfredo De Santis;

Probabilistic Visual Cryptography Schemes

Abstract

Visual cryptography schemes allow the encoding of a secret image, consisting of black or white pixels, into n shares which are distributed to the participants. The shares are such that only qualified subsets of participants can 'visually' recover the secret image. The secret pixels are shared with techniques that subdivide each secret pixel into a certain number m, m ≥ 2 of subpixels. Such a parameter m is called pixel expansion. Recently Yang introduced a probabilistic model. In such a model the pixel expansion m is 1, that is, there is no pixel expansion. The reconstruction of the image however is probabilistic, meaning that a secret pixel will be correctly reconstructed only with a certain probability. In this paper we propose a generalization of the model proposed by Yang. In our model we fix the pixel expansion m ≥ 1 that can be tolerated and we consider probabilistic schemes attaining such a pixel expansion. For m = 1 our model reduces to the one of Yang. For big enough values of m, for which a deterministic scheme exists, our model reduces to the classical deterministic model. We show that between these two extremes one can trade the probability factor of the scheme with the pixel expansion. Moreover, we prove that there is a one-to-one mapping between deterministic schemes and probabilistic schemes with no pixel expansion, where contrast is traded for the probability factor.

Country
Italy
Keywords

secret sharing schemes; contrast, Probabilistic schemes; Secret sharing; Visual cryptography

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
177
Top 10%
Top 1%
Top 10%
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