(Strong) Multi-Designated Verifiers Signatures Secure against Rogue Key Attack

Designated verifier signatures (DVS) allow a signer to create a signature whose validity can only be verified by a specific entity chosen by the signer. In addition, the chosen entity, known as the designated verifier, cannot convince any body that the signature is created by the signer. Multi-designated verifiers signatures (MDVS) are a natural extension of DVS in which the signer can choose multiple designated verifiers. DVS and MDVS are useful primitives in electronic voting and contract signing. In this paper, we investigate various aspects of MDVS and make two contributions. Firstly, we revisit the notion of unforgeability under rogue key attack on MDVS. In this attack scenario, a malicious designated verifier tries to forge a signature that passes through the verification of another honest designated verifier. A common counter-measure involves making the knowledge of secret key assumption (KOSK) in which an adversary is required to produce a proof-of-knowledge of the secret key. We strengthened the existing security model to capture this attack and propose a new construction that does not rely on the KOSK assumption. Secondly, we propose a generic construction of strong MDVS

Stronger Security and Constructions of Multi-Designated Verifier Signatures

Off-the-Record (OTR) messaging is a two-party message authentication protocol that also provides plausible deniability: there is no record that can later convince a third party what messages were actually sent. To extend OTR to group messaging we need to consider issues that are not present in the 2-party case. In group OTR (as in two-party OTR), the sender should be able to authenticate (or sign) his messages so that group members can verify who sent a message (that is, signatures should be unforgeable, even by group members). Also as in the two-party case, we want the off-the-record property: even if some verifiers are corrupt and collude, they should not be able to prove the authenticity of a message to any outsider. Finally, we need consistency, meaning that a corrupt sender cannot create confusion in the group as to what he said: if any group member accepts a signature, then all of them do. To achieve these properties it is natural to consider Multi-Designated Verifier Signatures (MDVS), which intuitively seem to target exactly the properties we require. However, existing literature defines and builds only limited notions of MDVS, where (a) the off-the-record property (referred to as source hiding) only holds when all verifiers could conceivably collude, and (b) the consistency property is not considered. The contributions of this paper are two-fold: stronger definitions for MDVS, and new constructions meeting those definitions. We strengthen source-hiding to support any subset of corrupt verifiers, and give the first formal definition of consistency. We give several constructions of our stronger notion of MDVS: one from generic standard primitives such as pseudorandom functions, pseudorandom generators, key agreement and NIZKs; one from specific instances of these primitives (for concrete efficiency); and one from functional encryption. The third construction requires an involved trusted setup step — including verification keys derived from a master secret — but this trusted setup buys us verifier-identity-based signing, for which such trusted setup is unavoidable. Additionally, in the third construction, the signature size can be made smaller by assuming a bound on colluding verifiers

APEX2S: A Two-Layer Machine Learning Model for Discovery of host-pathogen protein-protein Interactions on Cloud-based Multiomics Data

Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud-based multiomics data is discussed. In particular, a novel two-layer machine learning model, namely APEX2S, is proposed for discovery of the protein-protein interactions data. The results show that our model can better learn and predict from the accumulated host-pathogen protein-protein interactions

On the Black-Box Impossibility of Multi-Designated Verifiers Signature Schemes from Ring Signature Schemes

From the work by Laguillaumie and Vergnaud in ICICS\u2704, it has been widely believed that multi-designated verifier signature schemes (MDVS) can be constructed from ring signature schemes in general. However in this paper, somewhat surprisingly, we prove that it is impossible to construct an MDVS scheme from a ring signature scheme in a black-box sense (in the standard model). The impossibility stems from the difference between the definitions of unforgeability. To the best of our knowledge, existing works demonstrating the constructions do not provide formal reduction from an MDVS scheme to a ring signature scheme, and thus the impossibility has been overlooked for a long time

Redactable and Sanitizable Signature Schemes: Applications and Limitations for use in Decentralized Digital Identity Systems

Redactable signature schemes and sanitizable signature schemes are methods that permit modification of a given digital message and retain a valid signature. This can be applied to decentralized identity systems for delegating identity issuance and redacting sensitive information for privacy-preserving verification of identity. We propose implementing these protocols on a digital credential and compare them against other privacy-enhancing techniques to assess their suitabilityComment: Extended Abstract, 3 Pages, 1 Figure, International Conference on AI and the Digital Economy 202