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Privacy preserving in big data

Privacy preserving in big data

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Security and Privacy for Big Data, Cloud Computing and Applications — Recommend this title to your library

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Due to the rapid growth of computers and the technology that is capable of capturing data, the data is increasing exponentially; they are collected from the everyday interactions with digital products or services, including mobile devices, information sensing, social media and so on. Big data gives us unprecedented insights and opportunities, but the collected big data contains a large amount of personal or sensitive information, which raises big privacy concerns. This chapter provides an extensive literature review on privacy attack models, privacy-preserving technologies and privacy metrics. We systematically analysis how the data privacy can be disclosed, what kind of privacy technology has been developed and how to evaluate the privacy provided by the proposed privacy-preserving method.

Chapter Contents:

  • 1.1 Privacy attacks
  • 1.1.1 Tabular data attack
  • 1.1.1.1 Record linkage attack
  • 1.1.1.2 Attributes linkage attack
  • 1.1.1.3 Table linkage attack
  • 1.1.2 Graph data attack [3]
  • 1.1.2.1 Vertices degree
  • 1.1.2.2 Neighbourhood
  • 1.1.2.3 Embedded sub-graph
  • 1.1.2.4 Link relationship
  • 1.1.2.5 Attributes of vertices
  • 1.1.3 Location privacy attack
  • 1.1.3.1 Context linking attack
  • 1.1.3.2 Region intersection attack
  • 1.1.3.3 Machine-learning-based attack
  • 1.1.4 Attacks for other applications
  • 1.2 Privacy technologies
  • 1.2.1 Encryption
  • 1.2.1.1 Secure multiparty computing
  • 1.2.1.2 Homomorphic encryption
  • 1.2.2 Anonymization
  • 1.2.2.1 k-Anonymity
  • 1.2.2.2 l-Diversity
  • 1.2.2.3 t-Closeness
  • 1.2.3 Differential privacy
  • 1.2.3.1 Differential privacy
  • 1.2.3.2 Extensions of differential privacy
  • 1.2.4 Other technologies
  • 1.2.4.1 Caching
  • 1.2.4.2 Game theory
  • 1.3 Privacy metrics
  • 1.3.1 Uncertainty
  • 1.3.1.1 Anonymity parameter
  • 1.3.1.2 Entropy
  • 1.3.2 Error/Accuracy
  • 1.3.2.1 Error
  • 1.3.2.2 Accuracy
  • 1.3.3 Indistinguishability
  • 1.4 Summary
  • References

Inspec keywords: data privacy; Big Data

Other keywords: sensitive information; information sensing; mobile devices; personal information; everyday interactions; privacy-preserving technologies; collected big data; big privacy concerns; privacy metrics; unprecedented insights; privacy attack models; privacy technology; privacy-preserving method; data privacy; digital products

Subjects: Cryptography; Information networks; Data security

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