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020 _a9789811637643
_9978-981-16-3764-3
024 7 _a10.1007/978-981-16-3764-3
_2doi
050 4 _aQA76.9.A25
050 4 _aJC596-596.2
072 7 _aURD
_2bicssc
072 7 _aCOM060040
_2bisacsh
072 7 _aURD
_2thema
082 0 4 _a005.8
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082 0 4 _a323.448
_223
100 1 _aKim, Kwangjo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aPrivacy-Preserving Deep Learning
_h[electronic resource] :
_bA Comprehensive Survey /
_cby Kwangjo Kim, Harry Chandra Tanuwidjaja.
250 _a1st ed. 2021.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2021.
300 _aXIV, 74 p. 21 illus., 14 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs on Cyber Security Systems and Networks,
_x2522-557X
505 0 _aIntroduction -- Definition and Classification -- Background Knowledge -- X-based Hybrid PPDL -- The Gap Between Theory and Application of X-based PPDL -- Federated Learning and Split Learning-based PPDL -- Analysis and Performance Comparison -- Attacks on DL and PPDL as the Possible Solutions -- Challenges and Future Work.
520 _aThis book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.
650 0 _aData protection
_xLaw and legislation.
650 0 _aMachine learning.
650 0 _aData protection.
650 0 _aArtificial intelligence.
650 0 _aCloud Computing.
650 1 4 _aPrivacy.
650 2 4 _aMachine Learning.
650 2 4 _aData and Information Security.
650 2 4 _aArtificial Intelligence.
650 2 4 _aCloud Computing.
700 1 _aTanuwidjaja, Harry Chandra.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811637636
776 0 8 _iPrinted edition:
_z9789811637650
830 0 _aSpringerBriefs on Cyber Security Systems and Networks,
_x2522-557X
856 4 0 _uhttps://doi.org/10.1007/978-981-16-3764-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c177906
_d177906