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Privacy Metrics

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 22 Accesses

Synonyms

Privacy measures

Definition

Measures to determine the susceptibility of data or a dataset to revealing private information. Measures include ability to link private data to an individual, the level of detail or correctness of sensitive information, background information needed to determine private information, etc.

Historical Background

Legal definitions of privacy are generally based on the concept of Individually Identifiable Data. Unfortunately, this concept does not have a clear meaning in the context of many database privacy technologies. The official statistics (census) community has long been concerned with measures for privacy, particularly in the contexts of microdata sets (datasets that represent real data, but obscured in ways to protect privacy) and tabular datasets. Measures have largely been based on the probability that a specific value belongs to a given individual, given the disclosed data. As technologies have been developed to anonymize and analyze private...

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Recommended Reading

  1. Agrawal D, Aggarwal CC. On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2001. p. 247–55.

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  3. Li N, Li T. T-closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of the 23rd International Conference on Data Engineering; 2007.

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  4. Machanavajjhala A, Gehrke J, Kifer D, Venkitasubramaniam M. l-diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data. 2007;1(1): No.3.

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  5. Nergiz M, Atzori M, Clifton C. Hiding the presence of individuals from shared databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2007. p. 665–76.

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  6. Nergiz ME, Clifton C. Thoughts on k-anonymization. Data Knowl Eng. 2007;63(3):622–45.

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  7. Øhrn A, Ohno-Machado L. Using boolean reasoning to anonymize databases. Artif Intell Med. 1999;15(3):235–54.

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  8. Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertainty Fuzziness Knowledge Based Syst. 2002;10(5):557–70.

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Chris Clifton

Authors
  1. Chris Clifton
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Corresponding author

Correspondence to Chris Clifton .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Dept. of Computer Science, Purdue University, West Lafayette, IN, USA

    Chris Clifton

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Clifton, C. (2018). Privacy Metrics. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_272

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