000			nam a22 4500
999			_c33241 _d33241
008			240405b xxu||||| |||| 00| 0 eng d
020			_a9781009299510 _chbk.
082			_a005.8 _bCHE
100			_aChen, Kai
245			_aPrivacy-preserving computing for big data analytics and AI
260			_bCambridge University Press, _c2022 _aCambridge :
300			_axii, 255 p. ; _bill., _c24 cm.
365			_b49.99 _c₤ _d109.40
504			_aIncludes bibliographical references and index.
520			_aPrivacy-preserving computing aims to protect the personal information of users while capitalizing 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			_aBig data
650			_aUSENIX
650			_aPrivacy-preserving computing
650			_aSecure multiparty computation
650			_aTrusted Execution Environment
650			_aOblivious Transfer
650			_aBloom filter
650			_aCiphertext
650			_aData privacy
650			_aDifferential privacy
650			_aHomomorphic encryption
650			_aComputer security
650			_aBig data
650			_aInformation technology
650			_aSecurity measures privacy
650			_aRight of IT security
650			_aSecurity
650			_aIntel SGX
650			_aMachine learning
700			_aYang, Qiang
942			_2ddc _cBK