LEADER 03731nam 2200469 450 001 996464485403316 005 20210312091418.0 010 $a3-030-58896-3 024 7 $a10.1007/978-3-030-58896-0 035 $a(CKB)5410000000054458 035 $a(MiAaPQ)EBC6414297 035 $a(DE-He213)978-3-030-58896-0 035 $a(PPN)252510402 035 $a(EXLCZ)995410000000054458 100 $a20210312d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlgorithms for data and computation privacy /$fAlex X. Liu, Rui Li 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XVIII, 404 p. 163 illus., 130 illus. in color.) 311 $a3-030-58895-5 327 $aPart I Privacy Preserving Queries -- Range Queries over Encrypted Data -- Fast and Scalable Range and Keyword Query Processing over Encrypted Data with Provable Adaptive Security -- Nearest Neighbor Queries over Encrypted Data -- K-nearest Neighbor Queries over Encrypted Data -- Top-k Queries for Two-tiered Sensor Networks -- Part II Privacy Preserving Computation -- Collaborative Enforcement of Firewall Policies in Virtual Private Networks -- Privacy Preserving Quantification of Cross-Domain Network Reachability -- Cross-Domain Privacy-Preserving Cooperative Firewall Optimization -- Privacy Preserving String Matching for Cloud Computing -- Privacy Preserving Information Hub Identification in Social Networks -- Part III Differential Privacy -- Publishing Social Network Data with Privacy Guarantees -- Predictable Privacy-Preserving Mobile Crowd Sensing -- Differentially Private and Budget Limited Bandit Learning over Matroids -- Part IV Breaking Privacy -- Breaching Privacy in Encrypted Instant Messaging Networks. 330 $aThis book introduces the state-of-the-art algorithms for data and computation privacy. It mainly focuses on searchable symmetric encryption algorithms and privacy preserving multi-party computation algorithms. This book also introduces algorithms for breaking privacy, and gives intuition on how to design algorithm to counter privacy attacks. Some well-designed differential privacy algorithms are also included in this book. Driven by lower cost, higher reliability, better performance, and faster deployment, data and computing services are increasingly outsourced to clouds. In this computing paradigm, one often has to store privacy sensitive data at parties, that cannot fully trust and perform privacy sensitive computation with parties that again cannot fully trust. For both scenarios, preserving data privacy and computation privacy is extremely important. After the Facebook?Cambridge Analytical data scandal and the implementation of the General Data Protection Regulation by European Union, users are becoming more privacy aware and more concerned with their privacy in this digital world. This book targets database engineers, cloud computing engineers and researchers working in this field. Advanced-level students studying computer science and electrical engineering will also find this book useful as a reference or secondary text. 606 $aComputer security 606 $aComputer networks$xSecurity measures 615 0$aComputer security. 615 0$aComputer networks$xSecurity measures. 676 $a005.8 700 $aLiu$b Alex X.$01099803 702 $aLi$b Rui 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464485403316 996 $aAlgorithms for data and computation privacy$92814026 997 $aUNISA