Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143854
Title: A first study of compressive sensing for side-channel leakage sampling
Authors: Ou, Changhai
Zhou, Chengju
Lam, Siew-Kei
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Ou, C., Zhou, C., & Lam, S.-K. (2020). A first study of compressive sensing for side-channel leakage sampling. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(10), 2166-2177. doi:10.1109/TCAD.2019.2960337
Journal: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Abstract: An important prerequisite for Side-Channel Attacks (SCA) is leakage sampling where the side-channel measurements (i.e. power traces) of the cryptographic device are collected for further analysis. However, as the operating frequency of cryptographic devices continues to increase due to advancing technology, leakage sampling will impose higher requirements on the sampling rate and storage capacity of the sampling equipment. This paper undertakes the first study to show that effective leakage sampling can be achieved without relying on sophisticated equipments through Compressive Sensing (CS). As long as the information is leaked in the low frequency component, CS can obtain low-dimensional samples by simply projecting the high-dimensional signals onto the observation matrix. The power traces can then be reconstructed in a workstation for further analysis and storage. With this approach, the sampling rate to obtain power traces is no longer limited by the operating frequency of the cryptographic device and Nyquist sampling theorem. Instead it depends on the sparsity of the leakage signal. As such, CS can employ a much lower sampling rate and yet obtain equivalent leakage sampling performance, which significantly lowers the requirement of sampling equipments. The feasibility of our approach is verified theoretically and through experiments.
URI: https://hdl.handle.net/10356/143854
ISSN: 0278-0070
DOI: 10.1109/TCAD.2019.2960337
Schools: School of Computer Science and Engineering 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCAD.2019.2960337
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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