000 | 04356nam a22006135i 4500 | ||
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001 | 978-981-16-3764-3 | ||
003 | DE-He213 | ||
005 | 20240423125432.0 | ||
007 | cr nn 008mamaa | ||
008 | 210722s2021 si | s |||| 0|eng d | ||
020 |
_a9789811637643 _9978-981-16-3764-3 |
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024 | 7 |
_a10.1007/978-981-16-3764-3 _2doi |
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050 | 4 | _aQA76.9.A25 | |
050 | 4 | _aJC596-596.2 | |
072 | 7 |
_aURD _2bicssc |
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_aCOM060040 _2bisacsh |
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_aURD _2thema |
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082 | 0 | 4 |
_a005.8 _223 |
082 | 0 | 4 |
_a323.448 _223 |
100 | 1 |
_aKim, Kwangjo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 74 p. 21 illus., 14 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs on Cyber Security Systems and Networks, _x2522-557X |
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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. |
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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 |