000			02178cam a22003258i 4500
001			23397616
005			20250527154416.0
008			231128s2024 enk b 001 0 eng
010			_a 2023038105
020			_a9781009299510
020			_z9781009299534
040			_aDLC _beng _erda _cDLC _dDLC
042			_apcc
050	0	0	_aQA76.9.P735 _bC44 2024
082	0	0	_a005.8 CHE-K
100	1		_aChen, Kai
245	1	0	_aPrivacy-preserving computing for big data analytics and AI / _cKai Chen and Qiang Yang
260			_aUnited Kingdom _bCambridge University Press _c2022
300			_a255 p.
500			_aPrivacy-preserving computing aims to protect the personal information of users while capitalising on the possibilities unlocked by big data. This practical introduction for students, researchers, and industry practitioners is the first cohesive and systematic presentation of the field's advances over four decades. The book shows how to use privacy-preserving computing in real-world problems in data analytics and AI, and includes applications in statistics, database queries, and machine learning. The book begins by introducing cryptographic techniques such as secret sharing, homomorphic encryption, and oblivious transfer, and then broadens its focus to more widely applicable techniques such as differential privacy, trusted execution environment, and federated learning. The book ends with privacy-preserving computing in practice in areas like finance, online advertising, and healthcare, and finally offers a vision for the future of the field.
650		0	_aPrivacy-preserving techniques (Computer science)
650		0	_aComputer security.
650		0	_aData privacy.
650		0	_aArtificial intelligence.
700	1		_aYang, Qiang,
776	0	8	_iOnline version: _aChen, Kai, 1980- _tPrivacy-preserving computing for big data analytics and AI _dCambridge ; New York, NY, USA : Cambridge University Press, 2024 _z9781009299534 _w(DLC) 2023038106
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc
955			_brk21 2023-11-28 _irk21 2023-11-28 to rk09 for rev (then to PTCP for class/subj. proposal?) _ajs10 2023-12-12 to Dewey
999			_c93517 _d93517