Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor
DC Field | Value | Language |
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dc.contributor.author | Kim, Hanbit | - |
dc.contributor.author | Hong, Seokhie | - |
dc.contributor.author | Kim, HeeSeok | - |
dc.date.accessioned | 2021-09-01T16:22:41Z | - |
dc.date.available | 2021-09-01T16:22:41Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-04-01 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/66085 | - |
dc.description.abstract | A masking method is a widely known countermeasure against side-channel attacks. To apply a masking method to cryptosystems consisting of Boolean and arithmetic operations, such as ARX (Addition, Rotation, XOR) block ciphers, a masking conversion algorithm should be used. Masking conversion algorithms can be classified into two categories: Boolean to Arithmetic (B2A) and Arithmetic to Boolean (A2B). The A2B algorithm generally requires more execution time than the B2A algorithm. Using pre-computation tables, the A2B algorithm substantially reduces its execution time, although it requires additional space in RAM. In CHES2012, B. Debraize proposed a conversion algorithm that somewhat reduced the memory cost of using pre-computation tables. However, they still require (2(k+1)) entries of length (k+1)-bit where k denotes the size of the processed data. In this paper, we propose a low-memory algorithm to convert A2B masking that requires only (2k)(k)-bit. Our contributions are three-fold. First, we specifically show how to reduce the pre-computation table from (k+1)-bit to (k)-bit, as a result, the memory use for the pre-computation table is reduced from (2(k+1))(k+1)-bit to (2k)(k)-bit. Second, we optimize the execution times of the pre-computation phase and the conversion phase, and determine that our pre-computation algorithm requires approximately half of the operations than Debraize's algorithm. The results of the 8/16/32-bit simulation show improved speed in the pre-computation phase and the conversion phase as compared to Debraize's results. Finally, we verify the security of the algorithm against side-channel attacks as well as the soundness of the proposed algorithm. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, Seokhie | - |
dc.contributor.affiliatedAuthor | Kim, HeeSeok | - |
dc.identifier.doi | 10.3390/app9071438 | - |
dc.identifier.scopusid | 2-s2.0-85064087110 | - |
dc.identifier.wosid | 000466547500173 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.9, no.7 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 9 | - |
dc.citation.number | 7 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | ARX block ciphers | - |
dc.subject.keywordAuthor | Arithmetic to Boolean masking | - |
dc.subject.keywordAuthor | side-channel attacks | - |
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