Paper Abstract and Keywords |
Presentation |
2024-03-22 11:20
Evaluation of Feature Inference Risk from Explainable AI metrics LIME and Shapley Values Ryotaro Toma, Hiroaki Kikuchi (Meiji Univ.) ICSS2023-88 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Explainability has gained attention to ensure fairness and transparency in machine learning models, providing users with a sense of understanding. Many services such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure running Machine Learning as a Service (MLaaS) platforms, which provide several methods to explain model. However, in 2022, Luo et al. demonstrated that Shapley value-based explanations could lead to inference of private attribute, posing privacy risks of information leakage from models. Nevertheless, it remains unclear whether the attribute inference risk on the alternative explainability exist or not. Therefore, this study evaluates the attribute inference risk on LIME and compare the vulnerability with the explanability Shapley values. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
LIME / Shapley values / XAI / Explainability / Machine Learning / Feature Inference / / |
Reference Info. |
IEICE Tech. Rep., vol. 123, no. 448, ICSS2023-88, pp. 137-144, March 2024. |
Paper # |
ICSS2023-88 |
Date of Issue |
2024-03-14 (ICSS) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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ICSS2023-88 |
Conference Information |
Committee |
ICSS IPSJ-SPT |
Conference Date |
2024-03-21 - 2024-03-22 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
OIST |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Security, Trust, etc. |
Paper Information |
Registration To |
ICSS |
Conference Code |
2024-03-ICSS-SPT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Evaluation of Feature Inference Risk from Explainable AI metrics LIME and Shapley Values |
Sub Title (in English) |
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LIME |
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Shapley values |
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XAI |
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Explainability |
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Machine Learning |
Keyword(6) |
Feature Inference |
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1st Author's Name |
Ryotaro Toma |
1st Author's Affiliation |
Meiji University (Meiji Univ.) |
2nd Author's Name |
Hiroaki Kikuchi |
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Meiji University (Meiji Univ.) |
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Speaker |
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Date Time |
2024-03-22 11:20:00 |
Presentation Time |
25 minutes |
Registration for |
ICSS |
Paper # |
ICSS2023-88 |
Volume (vol) |
vol.123 |
Number (no) |
no.448 |
Page |
pp.137-144 |
#Pages |
8 |
Date of Issue |
2024-03-14 (ICSS) |
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