Privacy-Preserving Machine Learning / / by Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li

• Record Nr.: UNINA9910993945703321
• Autore: Li Jin
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
• ISBN: 9789811691393; 9811691398; 9789811691386; 981169138X
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (VIII, 88 p. 21 illus., 18 illus. in color.)
• Collana: SpringerBriefs on Cyber Security Systems and Networks, , 2522-557X
• Disciplina: 005.8; 323.448
• Soggetti: Data protection - Law and legislation; Machine learning; Privacy; Machine Learning; Aprenentatge automàtic; Seguretat informàtica; Protecció de dades; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Introduction -- Secure Cooperative Learning in Early Years -- Outsourced Computation for Learning -- Secure Distributed Learning -- Learning with Differential Privacy -- Applications - Privacy-Preserving Image Processing -- Threats in Open Environment -- Conclusion.
• Sommario/riassunto: 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.