Group Testing and Batch Verification

Zaverucha, Gregory M.; Stinson, Douglas R.

doi:10.1007/978-3-642-14496-7_12

Cited by 24 publications

(24 citation statements)

References 41 publications

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“…On the implementation of AutoBatch, future work could be more resilient to DoS and related attacks by implementing alternative techniques for recognizing invalid signatures in a batch, e.g., [42,50,51,69]. We are continuously on the lookout for more efficient means of computing in bilinear groups.…”

Section: Resultsmentioning

confidence: 99%

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Machine-generated algorithms, proofs and software for the batch verification of digital signature schemes

Akinyele

Green

Hohenberger

et al. 2012

Proceedings of the 2012 ACM Conference on Computer and Communications Security

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As devices everywhere increasingly communicate with each other, many security applications will require low-bandwidth signatures that can be processed quickly. Pairing-based signatures can be very short, but are often costly to verify. Fortunately, they also tend to have efficient batch verification algorithms. Finding these batching algorithms by hand, however, can be tedious and error prone.We address this by presenting AutoBatch, an automated tool for generating batch verification code in either Python or C++ from a high level representation of a signature scheme. AutoBatch outputs both software and, for transparency, a LaTeX file describing the batching algorithm and arguing that it preserves the unforgeability of the original scheme.We tested AutoBatch on over a dozen pairing-based schemes to demonstrate that a computer could find competitive batching solutions in a reasonable amount of time. In particular, it found an algorithm that is faster than a batching algorithm from Eurocrypt 2010. Another novel contribution is that it handles cross-scheme batching, where it searches for a common algebraic structure between two distinct schemes and attempts to batch them together.In this work, we expand upon our paper on AutoBatch appearing in ACM CCS 2012 [2] in a number of ways. We add a new loop-unrolling technique and show that it helps cut the batch verification cost of one scheme by roughly half. We describe our pruning and search algorithms in greater detail, including pseudocode and diagrams. All experiments were also re-run using the RELIC pairing library. We compare those results to our earlier results using the MIRACL library, and discuss why RELIC outperforms MIRACL in all but two cases. Automated proofs of several new batching algorithms are also included.AutoBatch is a useful tool for cryptographic designers and implementors, and to our knowledge, it is the first attempt to outsource to machines the design, proof writing and implementation of signature batch verification schemes.

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Section: Resultsmentioning

confidence: 99%

“…Prior works considered the implication of invalid signatures in a batch, e.g., [28,42,50,51,69]. Mainly, these works estimated raw signature verification times under various conditions.…”

Section: Batch Verification In Practicementioning

confidence: 99%

Machine-generated algorithms, proofs and software for the batch verification of digital signature schemes

Akinyele

Green

Hohenberger

et al. 2012

Proceedings of the 2012 ACM Conference on Computer and Communications Security

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“…Therefore, as the service provider has to check each item in the list, verification takes a (maximum) number of resources linear with the number of revoked credentials. Batch verification techniques try to tackle this [23]. Note that in some VLR schemes [33,21], all signatures made with the same credential become linkable after its revocation.…”

Section: Service Providermentioning

confidence: 99%

Analysis of Revocation Strategies for Anonymous Idemix Credentials

Lapon¹,

Kohlweiss

Decker

et al. 2011

Communications and Multimedia Security

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Abstract.In an increasing information-driven society, preserving privacy is essential. Anonymous credentials promise a solution to protect the user's privacy. However, to ensure accountability, efficient revocation mechanisms are essential. Having classified existing revocation strategies, we implemented one variant for each. In this paper we describe our classification and compare our implementations. Finally, we present a detailed analysis and pragmatic evaluation of the strategies.

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“…Aside from terminology, a "group testing" algorithm is precisely a forgery-identification algorithm built on top of batch verification; in particular, both [25] and [26] fit into this framework. However, the following papers (some of which predate [39]) do not fit into this framework.…”

Section: Introductionmentioning

confidence: 99%

“…Zaverucha and Stinson in [39] pointed out that there was already a long literature on the number of tests required by adaptive and non-adaptive "group testing" algorithms. Aside from terminology, a "group testing" algorithm is precisely a forgery-identification algorithm built on top of batch verification; in particular, both [25] and [26] fit into this framework.…”

Section: Introductionmentioning

confidence: 99%

Faster Batch Forgery Identification

Bernstein

Doumen

Lange

et al. 2012

Lecture Notes in Computer Science

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Abstract. Batch signature verification detects whether a batch of signatures contains any forgeries. Batch forgery identification pinpoints the location of each forgery. Existing forgery-identification schemes vary in their strategies for selecting subbatches to verify (individual checks, binary search, combinatorial designs, etc.) and in their strategies for verifying subbatches. This paper exploits synergies between these two levels of strategies, reducing the cost of batch forgery identification for ellipticcurve signatures.

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Group Testing and Batch Verification

Cited by 24 publications

References 41 publications

Machine-generated algorithms, proofs and software for the batch verification of digital signature schemes

Machine-generated algorithms, proofs and software for the batch verification of digital signature schemes

Analysis of Revocation Strategies for Anonymous Idemix Credentials

Faster Batch Forgery Identification

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