Cryptographic Hash Function using current-induced magnetization switching in nano-magnetic devices

Spintronics-based devices for hardware security primitives have gained much interest due to their unique physical characteristics. In this work, the Cryptographic hash function (CHF) generation system uses spintronic devices, specifically current-induced nano-magnetic structures. The system models the device behavior with the Boltzmann equation, incorporating Gaussian noise to simulate process variations around the device’s inflection point (IP). Simulated outputs are binarized into 4-bit strings and used for generating SHA-1 and SHA-256 hash outputs. Post-processing via XOR-based whitening improves randomness, reducing inter-collision rates and enhancing security metrics. The effect of device-to-device (D2D) variations is considered to ensure robustness across multiple devices. Performance metrics like collision rate, avalanche effect, and entropy are evaluated, confirming the system’s robustness and scalability in hardware security applications.

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Publication Details

Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11044006
Primary Topic
Advanced Memory and Neural Computing
Type
article
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Cryptographic Hash Function using current-induced magnetization switching in nano-magnetic devices

Divyanshu Divyanshu, Selma Amara, Meng Tang, Gianluca Setti et al.
1 links
Advanced Memory and Neural Computing
article

Cryptographic Hash Function using current-induced magnetization switching in nano-magnetic devices

Divyanshu Divyanshu, Selma Amara, Meng Tang, Gianluca Setti, Aijaz H. Lone, Daniel N. Rahimi
article en

Abstract

Spintronics-based devices for hardware security primitives have gained much interest due to their unique physical characteristics. In this work, the Cryptographic hash function (CHF) generation system uses spintronic devices, specifically current-induced nano-magnetic structures. The system models the device behavior with the Boltzmann equation, incorporating Gaussian noise to simulate process variations around the device’s inflection point (IP). Simulated outputs are binarized into 4-bit strings and used for generating SHA-1 and SHA-256 hash outputs. Post-processing via XOR-based whitening improves randomness, reducing inter-collision rates and enhancing security metrics. The effect of device-to-device (D2D) variations is considered to ensure robustness across multiple devices. Performance metrics like collision rate, avalanche effect, and entropy are evaluated, confirming the system’s robustness and scalability in hardware security applications.

King Abdullah University of Science and Technology (SA)
Affordable and clean energy
Openalex Percentile: Top 87%
Advanced Memory and Neural Computing
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