Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171825
Title: CoTree: a side-channel collision tool to push the limits of conquerable space
Authors: Ou, Changhai
He, Debiao
Qiao, Kexin
Zheng, Shihui
Lam, Siew-Kei
Zhang, Fan
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Ou, C., He, D., Qiao, K., Zheng, S., Lam, S. & Zhang, F. (2023). CoTree: a side-channel collision tool to push the limits of conquerable space. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems. https://dx.doi.org/10.1109/TCAD.2023.3288512
Journal: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Abstract: By introducing collision information into divide-and-conquer distinguishers, the existing collision-optimized side-channel attacks transform the given candidate space into a significantly smaller collision space, thus achieving more efficient key recovery. However, the candidates of the first several sub-keys shared by collision chains are still repeatedly detected, which happens very frequently and brings huge computational overhead. To alleviate this, we propose a highly-efficient collision-optimized attack named Collision Tree (CoTree). This collision detection tool exploits tree structure to store the chains created from the same sub-chain on the same branch, thus significantly reducing the storage requirements. It then benefits from the properties of both tree and collisions, and exploits a top-down tree building procedure and traverses each node only once when detecting their collisions with a candidate of the sub-key currently under consideration. Finally, unlike the traditional top-down node removal, CoTree launches a bottom-up branch removal procedure to remove the chains unsatisfying the collision conditions from the tree after traversing all the considered candidates of this sub-key, thus avoiding the traversal of the branches satisfying the collision condition. These strategies make our CoTree significantly alleviate the repetitive collision detection, and our experiments verify that it significantly outperforms the existing works.
URI: https://hdl.handle.net/10356/171825
ISSN: 0278-0070
DOI: 10.1109/TCAD.2023.3288512
Schools: School of Computer Science and Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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