AI-Finger: From Physical Unclonable Function to AI-Hardware Fingerprint

Qianqian Pan*, Jun Wu*

*この研究の対応する著者

研究成果: Conference contribution

抄録

With the development of artificial intelligence (AI) and electronic technologies, large AI models are promoted to process complex tasks, e.g. natural language processing, image identification, etc. Due to resource limitations, end devices are powerless in training complex large AI models and tend to adopt AI model services provided by resource-sufficient cloud servers, named Machine Learning as a Service (MLaaS). However, in MLaaS, there exists a critical smart data leakage issue, i.e. the illegal abuse of AI models without permission. Although several existing works design authentication and protection schemes for smart data in AI models, they require permanent storage of privacy keys, which suffer from privacy key leakage and abuse issues. Moreover, existing works mainly focus on pay-per-query for MLaaS, without the ability to support pay-per-device services. To solve the above issues, we propose a physical unclonable function (PUF)-empowered AI-hardware fingerprint approach to protect AI model intellectual property. First, a PUF-empowered AI model deep protection framework is proposed, including device-specific AI-hardware fingerprint-empowered authentication and MLaaS subscription/providing. Second, we propose an AI-hardware fingerprint-enabled end-device authentication protocol to support device-bind and key-storageless authentication. Third, based on the device-bind AI-hardware fingerprint, the pay-per-device MLaaS subscription and providing scheme is designed. Experimental results verify the reliability and effectiveness of the proposed PUF-based AI-hardware fingerprint approach.

本文言語English
ホスト出版物のタイトルProceedings - 2024 IEEE 10th Conference on Big Data Security on Cloud, BigDataSecurity 2024
出版社Institute of Electrical and Electronics Engineers Inc.
ページ167-172
ページ数6
ISBN(電子版)9798350389524
DOI
出版ステータスPublished - 2024
イベント10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024 - New York City, United States
継続期間: 2024 5月 102024 5月 12

出版物シリーズ

名前Proceedings - 2024 IEEE 10th Conference on Big Data Security on Cloud, BigDataSecurity 2024

Conference

Conference10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024
国/地域United States
CityNew York City
Period24/5/1024/5/12

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理

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