Practical side-channel based model extraction attack on tree-based machine learning algorithm

Dirmanto Jap, Ville Oskari Yli Maeyry, Akira Ito, Rei Ueno, Shivam Bhasin, Naofumi Homma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning algorithms have been widely applied to solve various type of problems and applications. Among those, decision tree based algorithms have been considered for small Internet-of-Things (IoT) implementation, due to their simplicity. It has been shown in a recent publication, that Bonsai, a small tree-based algorithm, can be successfully fitted in a small 8-bit microcontroller. However, the security of machine learning algorithm has also been a major concern, especially with the threat of secret parameter recovery which could lead to breach of privacy. With machine learning taking over a significant proportion of industrial tasks, the security issue has become a matter of concern. Recently, secret parameter recovery for neural network based algorithm using physical side-channel leakage has been proposed. In the paper, we investigate the security of widely used decision tree algorithms running on ARM Cortex M3 platform against electromagnetic (EM) side-channel attacks. We show that by focusing on each building block function or component, one could perform divide-and-conquer approach to recover the secret parameters. To demonstrate the attack, we first report the recovery of secret parameters of Bonsai, such as, sparse projection parameters, branching function and node predictors. After the recovery of these parameters, the attacker can then reconstruct the whole architecture.

Original languageEnglish
Title of host publicationApplied Cryptography and Network Security Workshops - ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S and P, SCI, SecMT, and SiMLA, Proceedings
EditorsJianying Zhou, Chuadhry Mujeeb Ahmed, Mauro Conti, Eleonora Losiouk, Man Ho Au, Lejla Batina, Zhou Li, Jingqiang Lin, Bo Luo, Suryadipta Majumdar, Weizhi Meng, Martín Ochoa, Stjepan Picek, Georgios Portokalidis, Cong Wang, Kehuan Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages93-105
Number of pages13
ISBN (Print)9783030616373
DOIs
Publication statusPublished - 2020
Event2nd ACNS Workshop on Application Intelligence and Blockchain Security, AIBlock 2020, 1st ACNS Workshop on Artificial Intelligence in Hardware Security, AIHWS 2020, 2nd ACNS Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2020, 2nd ACNS Workshop on Cloud Security and Privacy, Cloud S and P 2020, 1st ACNS Workshop on Secure Cryptographic Implementation, SCI 2020, 1st ACNS Workshop on Security in Mobile Technologies, SecMT 2020, and 2nd ACNS Workshop on Security in Machine Learning and its Applications, SiMLA 2020, held in parallel with the 18th International Conference on Applied Cryptography and Network Security, ACNS 2020 - Rome, Italy
Duration: 2020 Oct 192020 Oct 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12418 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd ACNS Workshop on Application Intelligence and Blockchain Security, AIBlock 2020, 1st ACNS Workshop on Artificial Intelligence in Hardware Security, AIHWS 2020, 2nd ACNS Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2020, 2nd ACNS Workshop on Cloud Security and Privacy, Cloud S and P 2020, 1st ACNS Workshop on Secure Cryptographic Implementation, SCI 2020, 1st ACNS Workshop on Security in Mobile Technologies, SecMT 2020, and 2nd ACNS Workshop on Security in Machine Learning and its Applications, SiMLA 2020, held in parallel with the 18th International Conference on Applied Cryptography and Network Security, ACNS 2020
CountryItaly
CityRome
Period20/10/1920/10/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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