Lightweight and Practical Anonymous Authentication Protocol for RFID systems using physically unclonable functions

Radio frequency identification (RFID) has been considered one of the imperative requirements for implementation of Internet-of-Things applications. It helps to solve the identification issues of the things in a cost-effective manner, but RFID systems often suffer from various security and privacy issues. To solve those issues for RFID systems, many schemes have been recently proposed by using the cryptographic primitive, called physically uncloneable functions (PUFs), which can ensure a tamper-evident feature. However, to the best of our knowledge, none of them has succeeded to address the problem of privacy preservation with the resistance of DoS attacks in a practical way. For instance, existing schemes need to rely on exhaustive search operations to identify a tag, and also suffer from several security and privacy related issues. Furthermore, a tag needs to store some security credentials (e.g., secret shared keys), which may cause several issues such as loss of forward and backward secrecy and large storage costs. Therefore, in this paper, we first propose a lightweight privacy-preserving authentication protocol for the RFID system by considering the ideal PUF environment. Subsequently, we introduce an enhanced protocol which can support the noisy PUF environment. It is argued that both of our protocols can overcome the limitations of existing schemes, and further ensure more security properties. By analyzing the performance, we have shown that the proposed solutions are secure, efficient, practical, and effective for the resource-constraint RFID tag

The Role of the Adversary Model in Applied Security Research

Adversary models have been integral to the design of provably-secure cryptographic schemes or protocols. However, their use in other computer science research disciplines is relatively limited, particularly in the case of applied security research (e.g., mobile app and vulnerability studies). In this study, we conduct a survey of prominent adversary models used in the seminal field of cryptography, and more recent mobile and Internet of Things (IoT) research. Motivated by the findings from the cryptography survey, we propose a classification scheme for common app-based adversaries used in mobile security research, and classify key papers using the proposed scheme. Finally, we discuss recent work involving adversary models in the contemporary research field of IoT. We contribute recommendations to aid researchers working in applied (IoT) security based upon our findings from the mobile and cryptography literature. The key recommendation is for authors to clearly define adversary goals, assumptions and capabilities