001461431 000__ 03833cam\a2200625\i\4500
001461431 001__ 1461431
001461431 003__ OCoLC
001461431 005__ 20230503003352.0
001461431 006__ m\\\\\o\\d\\\\\\\\
001461431 007__ cr\cn\nnnunnun
001461431 008__ 230315s2023\\\\si\a\\\\o\\\\\000\0\eng\d
001461431 019__ $$a1372621337$$a1373233161
001461431 020__ $$a9789811986925$$q(electronic bk.)
001461431 020__ $$a9811986924$$q(electronic bk.)
001461431 020__ $$z9789811986918
001461431 020__ $$z9811986916
001461431 0247_ $$a10.1007/978-981-19-8692-5$$2doi
001461431 035__ $$aSP(OCoLC)1372630839
001461431 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dUKAHL$$dOCLCF$$dN$T
001461431 049__ $$aISEA
001461431 050_4 $$aQA76.9.A25
001461431 08204 $$a005.8$$223/eng/20230315
001461431 1001_ $$aYu, Shui$$c(Computer scientist),$$eauthor.
001461431 24510 $$aSecurity and privacy in federated learning /$$cShui Yu, Lei Cui.
001461431 264_1 $$aSingapore :$$bSpringer,$$c2023.
001461431 300__ $$a1 online resource (170 pages) :$$billustrations (some color).
001461431 336__ $$atext$$btxt$$2rdacontent
001461431 337__ $$acomputer$$bc$$2rdamedia
001461431 338__ $$aonline resource$$bcr$$2rdacarrier
001461431 4901_ $$aDigital privacy and security
001461431 5050_ $$aChapter 1. Introduction of Federated Learning -- Chapter 2. Inference Attacks and Counter Attacks in Federated Learning -- Chapter 3. Poisoning Attacks and Counter Attacks in Federated Learning -- Chapter 4. GAN Attacks and Counter Attacks in Federated Learning -- Chapter 5. Differential Privacy in Federated Learning -- Chapter 6. Secure Multi-Party Computation in Federated Learning -- Chapter 7. Secure Data Aggregation in Federated Learning -- Chapter 8. Anonymous Communication and Shuffle Model in Federated Learning -- Chapter 9. The Future Work.
001461431 506__ $$aAccess limited to authorized users.
001461431 520__ $$aIn this book, the authors highlight the latest research findings on the security and privacy of federated learning systems. The main attacks and counterattacks in this booming field are presented to readers in connection with inference, poisoning, generative adversarial networks, differential privacy, secure multi-party computation, homomorphic encryption, and shuffle, respectively. The book offers an essential overview for researchers who are new to the field, while also equipping them to explore this uncharted territory. For each topic, the authors first present the key concepts, followed by the most important issues and solutions, with appropriate references for further reading. The book is self-contained, and all chapters can be read independently. It offers a valuable resource for masters students, upper undergraduates, Ph.D. students, and practicing engineers alike.
001461431 588__ $$aDescription based on print version record.
001461431 650_0 $$aComputer security.
001461431 650_0 $$aData privacy.
001461431 650_0 $$aMachine learning.
001461431 655_0 $$aElectronic books.
001461431 7001_ $$aCui, Lei,$$eauthor.
001461431 77608 $$iPrint version:$$aYu, Shui (Computer scientist).$$tSecurity and privacy in federated learning.$$dSingapore : Springer Nature Singapore, 2023$$z9789811986918$$w(OCoLC)1359608003
001461431 830_0 $$aDigital privacy and security.
001461431 852__ $$bebk
001461431 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-8692-5$$zOnline Access$$91397441.1
001461431 909CO $$ooai:library.usi.edu:1461431$$pGLOBAL_SET
001461431 980__ $$aBIB
001461431 980__ $$aEBOOK
001461431 982__ $$aEbook
001461431 983__ $$aOnline
001461431 994__ $$a92$$bISE