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Artificial Intelligence Security and Privacy

First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, Guangzhou, China, December 3–5, 2023, Proceedings, Part I

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Jaideep Vaidya

    1. Rutgers University, Newark, USA

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  2. Moncef Gabbouj

    1. Tampere University, Tampere, Finland

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    You can also search for this editor in PubMed   Google Scholar

  3. Jin Li

    1. Guangzhou University, Guangzhou, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14509)

Included in the following conference series:

Conference proceedings info: AIS&P 2023.

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About this book

This two-volume set LNCS 14509-14510, constitutes the refereed proceedings of the First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, held in Guangzhou, China, during December 3–5, 2023.

The 40 regular papers and 23 workshop papers presented in this  two-volume set were carefully reviewed and selected from 115 submissions.

Topics of interest include, e.g., attacks and defence on AI systems; adversarial learning; privacy-preserving data mining; differential privacy; trustworthy AI; AI fairness; AI interpretability; cryptography for AI; security applications.

 


Keywords

Table of contents (40 papers)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Fine-Grained Searchable Encryption Scheme Against Keyword Brute-Force Attacks

    • Yawen Feng, Shengke Zeng, Jixiang Xiao, Shuai Cheng, Fengchun Zhang
    Pages 1-15

  3. Fine-Grained Authorized Secure Deduplication with Dynamic Policy

    • Jixiang Xiao, Shengke Zeng, Yawen Feng, Shuai Cheng
    Pages 16-32

  4. Deep Multi-image Hiding with Random Key

    • Wei Zhang, Weixuan Tang, Yuan Rao, Bin Li, Jiwu Huang
    Pages 33-41

  5. Member Inference Attacks in Federated Contrastive Learning

    • Zixin Wang, Bing Mi, Kongyang Chen
    Pages 42-52

  6. A Network Traffic Anomaly Detection Method Based on Shapelet and KNN

    • Si Yu, Xin Xie, Zhao Li, Wenbing Zhen, Tijian Cai
    Pages 53-64

  7. Multi-channel Deep Q-network Carrier Sense Multiple Access

    • Xunxun Pi, Junhang Qiu
    Pages 65-80

  8. DFaP: Data Filtering and Purification Against Backdoor Attacks

    • Haochen Wang, Tianshi Mu, Guocong Feng, ShangBo Wu, Yuanzhang Li
    Pages 81-97

  9. A Survey of Privacy Preserving Subgraph Matching Methods

    • Xingjiang Cheng, Fuxing Zhang, Yun Peng, Xianmin Wang, Teng Huang, Ziye Zhou et al.
    Pages 98-113

  10. The Analysis of Schnorr Multi-Signatures and the Application to AI

    • Wenchao Wang, Jing Qin, Jinlu Liu, Xi Zhang, Xinyi Hou, Zhongkai Wei
    Pages 114-130

  11. Active Defense Against Image Steganography

    • Weixuan Tang, Yadong Liu
    Pages 131-141

  12. Strict Differentially Private Support Vector Machines with Dimensionality Reduction

    • Teng Wang, Shuanggen Liu, Jiangguo Liang, Shuai Wang, Lu Wang, Junying Song
    Pages 142-155

  13. Converging Blockchain and Deep Learning in UAV Network Defense Strategy: Ensuring Data Security During Flight

    • Zhihao Li, Qi Chen, Weichuan Mo, Xiaolin Wang, Li Hu, Yongzhi Cao
    Pages 156-171

  14. Towards Heterogeneous Federated Learning: Analysis, Solutions, and Future Directions

    • Yongwei Lin, Yucheng Long, Zhili Zhou, Yan Pang, Chunsheng Yang
    Pages 172-189

  15. From Passive Defense to Proactive Defence: Strategies and Technologies

    • Chong Shi, Jiahao Peng, Shuying Zhu, Xiaojun Ren
    Pages 190-205

  16. Research on Surface Defect Detection System of Chip Inductors Based on Machine Vision

    • Xiao Li, Xunxun Pi, Hong Tang, Junhang Qiu
    Pages 206-220

  17. Multimodal Fatigue Detection in Drivers via Physiological and Visual Signals

    • Weijia Li, Xunxun Pi, Hong Tang, Junhang Qiu
    Pages 221-236

  18. Protecting Bilateral Privacy in Machine Learning-as-a-Service: A Differential Privacy Based Defense

    • Le Wang, Haonan Yan, Xiaodong Lin, Pulei Xiong
    Pages 237-252

  19. FedCMK: An Efficient Privacy-Preserving Federated Learning Framework

    • Pengyu Lu, Xianjia Meng, Ximeng Liu
    Pages 253-271

  20. An Embedded Cost Learning Framework Based on Cumulative Gradient Rewards

    • Weixuan Tang, Yingjie Xie
    Pages 272-282
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Other volumes

  1. Artificial Intelligence Security and Privacy

  2. Artificial Intelligence Security and Privacy

Editors and Affiliations

  • Rutgers University, Newark, USA

    Jaideep Vaidya

  • Tampere University, Tampere, Finland

    Moncef Gabbouj

  • Guangzhou University, Guangzhou, China

    Jin Li

Bibliographic Information

  • Book Title: Artificial Intelligence Security and Privacy

  • Book Subtitle: First International Conference on Artificial Intelligence Security and Privacy, AIS&P 2023, Guangzhou, China, December 3–5, 2023, Proceedings, Part I

  • Editors: Jaideep Vaidya, Moncef Gabbouj, Jin Li

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-981-99-9785-5

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

  • Softcover ISBN: 978-981-99-9784-8Published: 04 February 2024

  • eBook ISBN: 978-981-99-9785-5Published: 03 February 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XV, 595

  • Number of Illustrations: 20 b/w illustrations, 147 illustrations in colour

  • Topics: Artificial Intelligence, Security Science and Technology, Privacy, Cryptology, Security Services

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