LEADER 04355nam 22006375 450 001 9910254835403321 005 20200706102653.0 010 $a3-319-62004-5 024 7 $a10.1007/978-3-319-62004-6 035 $a(CKB)3710000001631405 035 $a(DE-He213)978-3-319-62004-6 035 $a(MiAaPQ)EBC4983327 035 $a(PPN)203850890 035 $a(EXLCZ)993710000001631405 100 $a20170822d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDifferential Privacy and Applications /$fby Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIII, 235 p. 71 illus.) 225 1 $aAdvances in Information Security,$x1568-2633 ;$v69 311 $a3-319-62002-9 320 $aIncludes bibliographical references and index. 327 $aPreliminary of Differential Privacy -- Differentially Private Data Publishing: Settings and Mechanisms -- Differentially Private Data Publishing: Interactive Setting -- Differentially Private Data Publishing: Non-interactive Setting -- Differentially Private Data Analysis -- Differentially Private Deep Learning -- Differentially Private Applications: Where to Start? -- Differentially Private Social Network Data Publishing -- Differentially Private Recommender System -- Privacy Preserving for Tagging Recommender Systems -- Differential Location Privacy -- Differentially Private Spatial Crowdsourcing -- Correlated Differential Privacy for Non-IID Datasets -- Future Directions. 330 $aThis book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful. 410 0$aAdvances in Information Security,$x1568-2633 ;$v69 606 $aData mining 606 $aComputer security 606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aSystems and Data Security$3https://scigraph.springernature.com/ontologies/product-market-codes/I28060 606 $aPrivacy$3https://scigraph.springernature.com/ontologies/product-market-codes/I28010 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aData mining. 615 0$aComputer security. 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aSystems and Data Security. 615 24$aPrivacy. 615 24$aArtificial Intelligence. 676 $a005.8 700 $aZhu$b Tianqing$4aut$4http://id.loc.gov/vocabulary/relators/aut$01059731 702 $aLi$b Gang$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZhou$b Wanlei$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aYu$b Philip S$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254835403321 996 $aDifferential Privacy and Applications$92507968 997 $aUNINA