We present a first-order probabilistic epistemic logic, which allows combining operators of knowledge and probability within a group of possibly infinitely many agents. We define its syntax and semantics and prove the strong completeness property of the corresponding axiomatic system. ¹

We address a class of authentication protocols called “HB” ones and the man-in-the-middle (MIM) attack, reported at the ASIACRYPT conference, called OOV-MIM (Ouafi-Overbeck-Vaudenay MIM). Analysis of the considered attack and its systematic experimental evaluation are given. It is shown that the main component of OOV-MIM, the algorithm for measuring the Hamming weight of noise vectors, outputs incorrect results as a consequence of the employed approximation of the probability distributions. The analysis reveals that, practically, the only scenario in which the OOV-MIM attack is effective is the one in which two incorrect estimations produced by the algorithm for measuring the Hamming weight, when coupled, give the correct result. This paper provides additional insights into the OOV-MIM and corrected claims about the performance/complexity showing that the performances of the considered attack have been overestimated, i.e., that the complexity of the attack has been underestimated. Particularly, the analysis points out the reasons for the incorrect claims and to the components of the attack that do not work as expected.

The problem of developing authentication protocols dedicated to a specific scenario where an entity with limited computational capabilities should prove the identity to a computationally powerful Verifier is addressed. An authentication protocol suitable for the considered scenario which jointly employs the learning parity with noise (LPN) problem and a paradigm of random selection is proposed. It is shown that the proposed protocol is secure against active attacking scenarios and so called GRS man-in-the-middle (MIM) attacking scenarios. In comparison with the related previously reported authentication protocols the proposed one provides reduction of the implementation complexity and at least the same level of the cryptographic security.

In wireless medical sensor networks (WMSNs), wearable or implantable sensors are used to collect vital body parameters, allowing remote monitoring and advanced predictive and preventive healthcare. This involves transmitting patients’ physiological data, which are sensitive and should be confidential, over the network. Thus, ensuring the security and privacy of these data is one of the most important prerequisites for the successful development of healthcare systems based on WMSN. In this regard, reliable authentication protocols designed for this specific scenario are needed. In this article, we propose the most efficient attacks to date on the widely referenced lightweight authentication protocols for WMSNs—Wang et al.’s protocol (2022). This protocol relies on blockchain and smart contracts to address the issue of centralization and on physically unclonable functions (PUFs) to ensure advanced physical-layer security. Our attacks exploit the protocol’s vulnerabilities that stem from an inadequate use of the underlying blockchain component, unlike the previous attacks that require challenging conditions to meet, i.e., physical access to users’ mobile devices and specialized power analysis techniques. We introduce new attack scenarios and prove that Wang et al.’s protocol is vulnerable to impersonation, tracing, message replay, and session key disclosure. We validate these vulnerabilities using the ProVerif tool, thereby confirming the feasibility of the attack scenarios and refuting Wang et al.’s claims regarding the protocol’s security properties. In addition, we identify and discuss other design weaknesses in Wang et al.’s proposal that further undermine its overall security and practical applicability. Finally, we revisit the feasibility of previously published attacks on Wang et al.’s protocol.

This paper reconsiders a powerful man-in-the-middle attack against Random-HB# and HB# authentication protocols, two prominent representatives of the HB family of protocols, which are built based on the Learning Parity in Noise (LPN) problem. A recent empirical report pointed out that the attack does not meet the claimed precision and complexity. Performing a thorough theoretical and numerical re-evaluation of the attack, in this paper we identify the root cause of the detected problem, which lies in reasoning based on approximate probability distributions of the central attack events, that can not provide the required precision due to the inherent limitations in the use of the Central Limit Theorem for this particular application. We rectify the attack by employing adequate Bayesian reasoning, after establishing the exact distributions of these events, and overcome the mentioned limitations. We further experimentally confirm the correctness of the rectified attack and show that it satisfies the required, targeted accuracy and efficiency, unlike the original attack.