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Many gate-level masking styles have been published, each with pros and cons. Some styles such as RSM, GLUT, and ISW are compact but can feature 1st-order leakage. Some other styles, such as TI, DOM, and HPC are secure at the 1st-order but incur significant overheads in terms of performance. Another requirement is that security shall be ensured even when the device is aged. Pre-silicon security evaluation is now a normatively approved method to characterize the expected resiliency against attacks ahead of time. However, in this regard, there is still a fragmentation in terms of leakage models, Points of Interest (PoI) selection, attack order, and distinguishers. Accordingly, in this paper we focus on such factors as they affect the success of side-channel analysis attacks and assess the resiliency of the state-of-the-art masking styles in various corners. Moreover, we investigate the impact of device aging as another factor and analyze its influence on the success of side-channel attacks targeting the state-of-the-art masking schemes. This pragmatic evaluation enables risk estimation in a complex PPA (Power, Performance, and Area) and security plane while also considering aging impacts into account. For instance, we explore the trade-off between low-cost secure styles attackable at 1st-order vs high-cost protection attackable only at 2nd-order.<\/jats:p>","DOI":"10.1007\/s10836-024-06155-1","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T08:09:06Z","timestamp":1734336546000},"page":"723-740","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-modal Pre-silicon Evaluation of Hardware Masking Styles"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9302-413X","authenticated-orcid":false,"given":"Md Toufiq Hasan","family":"Anik","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6776-2542","authenticated-orcid":false,"given":"Hasin Ishraq","family":"Reefat","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9433-7576","authenticated-orcid":false,"given":"Wei","family":"Cheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5063-7964","authenticated-orcid":false,"given":"Jean-Luc","family":"Danger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5044-3534","authenticated-orcid":false,"given":"Sylvain","family":"Guilley","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-6637","authenticated-orcid":false,"given":"Naghmeh","family":"Karimi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"issue":"11","key":"6155_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3569577","volume":"55","author":"S Picek","year":"2023","unstructured":"Picek S, Perin G, Mariot L, Wu L, Batina L (2023) Sok: Deep learning-based physical side-channel analysis. 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