With the ongoing digitization, digital circuits have become increasingly present in everyday life. However, as circuits can be faulty, their verification poses a challenging but essential challenge. In contrast to formal verification techniques, simulation techniques fail to fully guarantee the correctness of a circuit. However, due to the exponential complexity of the verification problem, formal verification can fail due to time or space constraints. To overcome this challenge, recently (PFV) has been introduced. Here, it has been shown that several circuits and circuit classes can be formally verified in polynomial time and space. In general, these proofs have to be conducted manually, requiring a lot of time. However, in recent research, a method for automated PFV has been proposed, where a proof engine automatically generates human-readable proofs that show the polynomial size of a (BDD) for a given function. The engine analyses the BDD and finds a pattern, which is then proven by induction. In this article, we formalize the previously presented BDD patterns and propose algorithms for the pattern detection, establishing new possibilities for the automated proof generation for more complex functions. Furthermore, we show an exemplary proof that can be generated using the presented methods.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.
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http://dx.doi.org/10.1098/rsta.2023.0390 | DOI Listing |
Philos Trans A Math Phys Eng Sci
January 2025
Institute of Computer Science, University of Bremen, Bremen, Germany.
With the ongoing digitization, digital circuits have become increasingly present in everyday life. However, as circuits can be faulty, their verification poses a challenging but essential challenge. In contrast to formal verification techniques, simulation techniques fail to fully guarantee the correctness of a circuit.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
The proliferation of the Internet of Things (IoT) has worsened the challenge of maintaining data and user privacy. IoT end devices, often deployed in unsupervised environments and connected to open networks, are susceptible to physical tampering and various other security attacks. Thus, robust, efficient authentication and key agreement (AKA) protocols are essential to protect data privacy during exchanges between end devices and servers.
View Article and Find Full Text PDFSci Rep
January 2025
School of Basic Medical Sciences, Jiangxi Medical College, Nanchang University, No.461 Bayi Road, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
As one of the essential lignan derivative found in traditional Chinese medicinal herbs, secoisolariciresinol diglucoside (SDG) was proved to promote women's health through its phytoestrogenic properties. Increasingly studies indicated that this compound could be a potential drug capable of preventing estrogen-related diseases. Here, we aimed to investigate whether SDG can counteract cyclophosphamide (CTX) induced premature ovarian insufficiency (POI) and further explore its specific molecular mechanism.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Financial Information Security, Kookmin University, Seoul 02707, Republic of Korea.
The 5G-AKA protocol, a foundational component for 5G network authentication, has been found vulnerable to various security threats, including linkability attacks that compromise user privacy. To address these vulnerabilities, we previously proposed the 5G-AKA-Forward Secrecy (5G-AKA-FS) protocol, which introduces an ephemeral key pair within the home network (HN) to support forward secrecy and prevent linkability attacks. However, a re-evaluation uncovered minor errors in the initial BAN-logic verification and highlighted the need for more rigorous security validation using formal methods.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland.
Allostatic self-efficacy (ASE) represents a computational theory of fatigue and depression. In brief, it postulates that (i) fatigue is a feeling state triggered by a metacognitive diagnosis of loss of control over bodily states (persistently elevated interoceptive surprise); and that (ii) generalization of low self-efficacy beliefs beyond bodily control induces depression. Here, we converted ASE theory into a structural causal model (SCM).
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