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DBLP
Conference object . 2023
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Probabilistic Secret Sharing.

Authors: D’Arco, Paolo; De Prisco, Roberto; De Santis, Alfredo; Del Pozo, Angel Pérez; Vaccaro, Ugo;

Probabilistic Secret Sharing.

Abstract

In classical secret sharing schemes a dealer shares a secret among a set of participants in such a way that qualified subsets can reconstruct the secret, while forbidden ones do not get any kind of information about it. The basic parameter to optimize is the size of the shares, that is, the amount of secret information that the dealer has to give to participants. In this paper we formalize a notion of probabilistic secret sharing schemes, in which qualified subsets can reconstruct the secret but only with a certain controlled probability. We show that, by allowing a bounded error in the reconstruction of the secret, it is possible to drastically reduce the size of the shares the participants get (with respect to classical secret sharing schemes). We provide efficient constructions both for threshold access structures on a finite set of participants and for evolving threshold access structures, where the set of participants is potentially infinite. Some of our constructions yield shares of constant size (i.e., not depending on the number of participants) and an error probability of successfully reconstructing the secret which can be made as close to 1 as desired.

Countries
Germany, Italy
Keywords

evolving secret sharing, probabilistic secret sharing, Evolving secret sharing; Probabilistic secret sharing; Secret sharing; Software, Secret sharing, 004, ddc: ddc:004

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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!
0
Average
Average
Average
Green