rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Differential Privacy and Applications
  name:
    name part: Zhu, Tianqing.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Li, Gang.
    role:
      role term: author.
  name:
    name part: Zhou, Wanlei.
    role:
      role term: author.
  name:
    name part: Yu, Philip S.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 235 p. 71 illus. online resource.
  abstract: This 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.
  table of contents: Preliminary 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.
  note: by Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu.
  subject:
    topic: Computer science
  subject:
    topic: Computer security
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Systems and Data Security
  subject:
    topic: Privacy
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Information Security, 69
  identifier: 9783319620046
  identifier: http://dx.doi.org/10.1007/978-3-319-62004-6
  record info:
    record creation date: 170822
    record change date: 20170822154742.0
    record identifier: 978-3-319-62004-6