Biblio

In this paper, we extend the existing classification of signature models by Cao. To do so, we present a new signature classification framework and migrate the original classification to build an easily extendable faceted signature classification. We propose 20 new properties, 7 property families, and 1 signature classification type. With our classification, theoretically, up to 11 541 420 signature classes can be built, which should cover almost all existing signature schemes.

An oblivious signature is a digital signature with some property. The oblivious signature scheme has two parties, the signer and the receiver. First, the receiver can choose one and get one of n valid signatures without knowing the signer’s private key. Second, the signer does not know which signature is chosen by the receiver. In this paper, we propose the oblivious signature which is combined with blind signature and zero-knowledge set membership. The property of blind signature makes sure that the signer does not know the message of the signature by the receiver chosen, on the other hand, the property of the zero-knowledge set membership makes sure that the message of the signature by the receiver chosen is one of the set original messages.

Digital signatures are suitable technology for public key encryption. Acceptance (non-repudiation) of digital messages and data origin authentication are one of the main usage of digital signature. Digital signature's security mainly depends on the keys (public and private). These keys are used to generate and validate digital signatures. In digital signature signing process is performed using signer's secret key. However, any attacker can present a blinded version of message encrypted with signer's public key and can get the original message. Therefore, this paper proposed a novel method to identify blinded version of digital signature. The proposed method has been tested mathematically and found to be more efficient to detect blind signatures.

Many applications of mobile computing require the computation of dot-product of two vectors. For examples, the dot-product of an individual's genome data and the gene biomarkers of a health center can help detect diseases in m-Health, and that of the interests of two persons can facilitate friend discovery in mobile social networks. Nevertheless, exposing the inputs of dot-product computation discloses sensitive information about the two participants, leading to severe privacy violations. In this paper, we tackle the problem of privacy-preserving dot-product computation targeting mobile computing applications in which secure channels are hardly established, and the computational efficiency is highly desirable. We first propose two basic schemes and then present the corresponding advanced versions to improve efficiency and enhance privacy-protection strength. Furthermore, we theoretically prove that our proposed schemes can simultaneously achieve privacy-preservation, non-repudiation, and accountability. Our numerical results verify the performance of the proposed schemes in terms of communication and computational overheads.