No abstract
In this paper we present an analysis of the SpoC cipher, a second round candidate of the NIST Lightweight Crypto Standardization process. First we present a differential analysis on the sLiSCP-light permutation, a core element of SpoC. Then we propose a series of attacks on both versions of SpoC, namely round-reduced differential tag forgery and message recovery attacks, as well as a time-memory tradeoff key-recovery attack on the full round version of Spoc-64. Finally, we present an observation regarding the constants used in the sLiSCP-light permutation. To the best of our knowledge, this paper represents the first third-party analysis on both SpoC cipher and the sLiSCP-light permutation.
Physically Unclonable Functions (PUFs) are being proposed as a low cost alternative to permanently store secret keys or provide device authentication without requiring non-volatile memory, large e-fuses or other dedicated processing steps. In the literature, PUFs are split into two main categories. The so-called strong PUFs are mainly used for authentication purposes, hence also called authentication PUFs. They promise to be lightweight by avoiding extensive digital post-processing and cryptography. The socalled weak PUFs, also called key generation PUFs, can only provide authentication when combined with a cryptographic authentication protocol. Over the years, multiple research results have demonstrated that Strong PUFs can be modeled and attacked by machine learning techniques. Hence, the general assumption is that the security of a strong PUF is solely dependent on its security against machine learning attacks. The goal of this paper is to debunk this myth, by analyzing and breaking three recently published Strong PUFs (Suresh et al., VLSI Circuits 2020; Liu et al., ISSCC 2021; and Jeloka et al., VLSI Circuits 2017). The attacks presented in this paper have practical complexities and use generic symmetric key cryptanalysis techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.