Analysis and Design of Multiple Threshold Changeable Secret Sharing Schemes

Lou, Tiancheng; Tartary, Christophe

doi:10.1007/978-3-540-89641-8_14

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…In 1999, Martin et al [28] proposed the first TCSS. TCSSs can be classified into three types, schemes based on a linear polynomial [29,30], schemes based on the geometry [31], and schemes based on the CRT [32,33]. Since standard Shamir's SS is very simple and is unconditionally secure, most efforts have been devoted to propose TCSSs [29,34] to support standard Shamir's SS.…”

Section: Security Issues In the Secret Reconstructionmentioning

confidence: 99%

Dynamic threshold secret reconstruction and its application to the threshold cryptography

Harn

Hsu

2015

Information Processing Letters

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Section: Security Issues In the Secret Reconstructionmentioning

confidence: 99%

Dynamic threshold secret reconstruction and its application to the threshold cryptography

Harn

Hsu

2015

Information Processing Letters

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“…Definition 2 [5]. A perfect (t, n) threshold scheme is called an ideal (t, n) threshold scheme when the size of the shares is the same as the secret's.…”

Section: H S S H S =mentioning

confidence: 99%

“…threshold secret scheme Definition 1 [5]. A (t, n) threshold scheme is called perfect secret sharing scheme if it satisfies the following properties:…”

Section: B the Definition Of Perfect Secret Sharing Scheme And Idealmentioning

confidence: 99%

A novel anonymous secret sharing scheme based on BP Artificial Neural Network

Zhong

Wei

Shi

2012

2012 8th International Conference on Natural Computation

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In an anonymous secret sharing scheme, the secret can be reconstructed without knowledge of which participant hold which share. That is, in such scheme the secret can be recovered from the shares without the identities of shareholders. However, the previous anonymous schemes are not very efficient and the existing bounds make optimization work difficult.In this paper, we present a novel anonymous secret sharing scheme by using BP Artificial Neural Network. The scheme can ensure that the shareholders' identities are completely anonymous when the secret is recovered. Unlike previous schemes, our scheme is an ideal (t, n) threshold scheme which parameter t doesn't have lower bounds and need less storage space. Furthermore, the shares can be chosen by shareholders and the communication between dealer and participants doesn't need secure channel. In addition, the participants can verify the correctness of their shares and the secret S.

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“…In general, there are two different approaches to design a TCSS scheme without the assistance of an active dealer. The first approach requires the dealer to prepare the threshold changeability while generating shares for shareholders during set up [12,13]. For example, instead of providing only one share per shareholder using standard Shamir's secret-sharing scheme, the dealer needs to prepare multiple shares per shareholder or publish some public values during set up.…”

Section: Related Work On Threshold Changeable Secret Sharing (Tcss) mentioning

confidence: 99%

Generalised cheater detection and identification

Harn

2014

IET Information Security

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Cheater detection and identification are important issues in the process of secret reconstruction. To detect and identify cheaters most of the algorithms need the dealer to generate and distribute additional information to shareholders. Recently, algorithms have been proposed to detect and identify cheaters. If more than t (i.e. the threshold) shares, for example j (i.e. t < j) shares in the secret reconstruction, then redundancy of shares can be used to detect and identify cheaters. The detectability and identifiability of cheaters are proportional to the number of redundant shares. However, the number of redundant shares, j − t is fixed if original shares are used in the secret reconstruction. So, a threshold changeable verifiable secret sharing (TCVSS) has been developed, which allows shareholders working together to change the threshold t into a new threshold t′ (i.e. t′ < j) and generate new shares; whereas at the same time, maintain the original secret. The verifiability of the proposed TCVSS enables shareholders to verify that their new shares have been properly generated. The number of redundant shares can be changed to j − t′ if new shares are used in the secret reconstruction. Discussion on how to determine the new threshold t′ in order to detect and identify cheaters successfully has also been included.

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Analysis and Design of Multiple Threshold Changeable Secret Sharing Schemes

Cited by 13 publications

References 10 publications

Dynamic threshold secret reconstruction and its application to the threshold cryptography

Dynamic threshold secret reconstruction and its application to the threshold cryptography

A novel anonymous secret sharing scheme based on BP Artificial Neural Network

Generalised cheater detection and identification

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