| 書目名稱 | Privacy-Preserving Machine Learning |
| 編輯 | Jin Li,Ping Li,Tong Li |
| 視頻video | http://file.papertrans.cn/757/756071/756071.mp4 |
| 概述 | Offers a new research perspective on machine learning.Presents state-of-the-art techniques for privacy-preserving machine learning.Identifies potential security threats regarding machine learning-base |
| 叢書名稱 | SpringerBriefs on Cyber Security Systems and Networks |
| 圖書封面 |  |
| 描述 | .This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.. |
| 出版日期 | Book 2022 |
| 關(guān)鍵詞 | Machine Learning; Artificial Intelligence; Privacy-preserving Technique; Data Encryption; Neural Network |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-16-9139-3 |
| isbn_softcover | 978-981-16-9138-6 |
| isbn_ebook | 978-981-16-9139-3Series ISSN 2522-5561 Series E-ISSN 2522-557X |
| issn_series | 2522-5561 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
|Archiver|手機(jī)版|小黑屋|
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