Biblio

Universally Composable (UC) framework provides the strongest security notion for designing fully trusted cryptographic protocols, and it is very challenging on applying UC security in the design of RFID mutual authentication protocols. In this paper, we formulate the necessary conditions for achieving UC secure RFID mutual authentication protocols which can be fully trusted in arbitrary environment, and indicate the inadequacy of some existing schemes under the UC framework. We define the ideal functionality for RFID mutual authentication and propose the first UC secure RFID mutual authentication protocol based on public key encryption and certain trusted third parties which can be modeled as functionalities. We prove the security of our protocol under the strongest adversary model assuming both the tags' and readers' corruptions. We also present two (public) key update protocols for the cases of multiple readers: one uses Message Authentication Code (MAC) and the other uses trusted certificates in Public Key Infrastructure (PKI). Furthermore, we address the relations between our UC framework and the zero-knowledge privacy model proposed by Deng et al. [1].

EPC Gen2 tags are working as international RFID standards for the use in the supply chain worldwide, such tags are computationally weak devices and unable to perform even basic symmetric-key cryptographic operations. For this reason, to implement robust and secure pseudo-random number generators (PRNG) is a challenging issue for low-cost Radio-frequency identification (RFID) tags. In this paper, we study the security of LFSR-based PRNG implemented on EPC Gen2 tags and exploit LFSR-based PRNG to provide a better constructions. We provide a cryptanalysis against the J3Gen which is LFSR-based PRNG and proposed by Sugei et al. [1], [2] for EPC Gen2 tags using distinguish attack and make observations on its input using NIST randomness test. We also test the PRNG in EPC Gen2 RFID Tags by using the NIST SP800-22. As a counter-measure, we propose two modified models based on the security analysis results. We show that our results perform better than J3Gen in terms of computational and statistical property.