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Anonymization Techniques

  • Living reference work entry
  • First Online:
Encyclopedia of Big Data

  • 273 Accesses

Synonyms

Anonymous data; Data anonymization; Data privacy; De-Identification; Personally identifiable information

Introduction

Personal information is constantly being collected on individuals as they browse the internet or share data electronically. This collection of information has been further exacerbated with the emergence of the Internet of things and the connectivity of many electronic devices. As more data is disseminated into the world, interconnected patterns are created connecting one data record to the next. The massive data sets that are collected are of great value to businesses and data scientists alike. To properly protect the privacy of these individuals, it is necessary to de-identify or anonymize the data. In other words, personally identifiable information (PII) needs to be encrypted or altered so that a person’s sensitive data remains indiscernible to outside sources and readable to the pre-approved parties. Some popular anonymization techniques include noise...

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Further Reading

  • Dwork, C. (2006). Differential privacy. In Automata, languages and programming. Berlin: Springer.

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  • Li, Ninghui, et al. (2007). t-Closeness: Privacy beyond k-anonymity and l-diversity. IEEE 23rd International Conference on Data Engineering, 7.

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  • Machanavajjhala, A., et al. (2007). l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data, 1(1), Article 3, 1–12.

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  • Sweeney, L. (2002). k-anonymity: A Model for Protecting Privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5).

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  • The European Parliament and of the Council Working Party. (2014). Opinion 05/2014 on anonymisation techniques. http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2014/wp216_en.pdf. Retrieved on 29 Dec 2014.

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Author information

Authors and Affiliations

  1. North Carolina A&T State University, 1601 E Market St, Greensboro, NC, 27401, USA

    Mick Smith

  2. Information Technology Laboratory, US Army Engineer Research and Development Center, 3909 Halls Ferry Rd, Vicksburg, MS, 39180, USA

    Rajeev Agrawal

Authors
  1. Mick Smith
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  2. Rajeev Agrawal
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Corresponding author

Correspondence to Mick Smith .

Editor information

Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Connie L. McNeely

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© 2017 Springer International Publishing AG

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Smith, M., Agrawal, R. (2017). Anonymization Techniques. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_9-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_9-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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