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Federated Learning in Side-Channel Analysis

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Information Security and Cryptology – ICISC 2020 (ICISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12593))

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Abstract

Recently introduced federated learning is an attractive framework for the distributed training of deep learning models with thousands of participants. However, it can potentially be used with malicious intent. For example, adversaries can use their smartphones to jointly train a classifier for extracting secret keys from the smartphones’ SIM cards without sharing their side-channel measurements with each other. With federated learning, each participant might be able to create a strong model in the absence of sufficient training data. Furthermore, they preserve their anonymity. In this paper, we investigate this new attack vector in the context of side-channel attacks. We compare the federated learning, which aggregates model updates submitted by N participants, with two other aggregating approaches: (1) training on combined side-channel data from N devices, and (2) using an ensemble of N individually trained models. Our first experiments on 8-bit Atmel ATxmega128D4 microcontroller implementation of AES show that federated learning is capable of outperforming the other approaches.

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Acknowledgment

This work was supported in part by the research grant 2018-04482 from the Swedish Research Council.

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Authors and Affiliations

  1. KTH Royal Institute of Technology, Stockholm, Sweden

    Huanyu Wang & Elena Dubrova

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  1. Huanyu Wang
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  2. Elena Dubrova
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Correspondence to Huanyu Wang .

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  1. Jeonbuk National University, Jeonju-si, Korea (Republic of)

    Deukjo Hong

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Wang, H., Dubrova, E. (2021). Federated Learning in Side-Channel Analysis. In: Hong, D. (eds) Information Security and Cryptology – ICISC 2020. ICISC 2020. Lecture Notes in Computer Science(), vol 12593. Springer, Cham. https://doi.org/10.1007/978-3-030-68890-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-68890-5_14

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