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Privacy Models and Disclosure Risk Measures

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Data Privacy: Foundations, New Developments and the Big Data Challenge

Part of the book series: Studies in Big Data ((SBD,volume 28))

Abstract

A fundamental issue in order to define effective methods for ensuring confidentiality is to define privacy models as well as measures for disclosure risk assessment. In this chapter we review the main models and measures.

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Notes

  1. 1.

    This way of proceeding is already discussed in e.g. [29] (p. 408): “Methods that mask the key variables impede identification of the respondent in the file, and methods that mask the target variables limit what is learned if a match is made. Both approaches may be useful, and in practice a precise classification of variables as keys or targets may be difficult. However, masking of targets is more vulnerable to the trade-off between protection gain and information loss than masking of keys; hence masking of keys seems potentially more fruitful”.

  2. 2.

    In databases, the schema define the type of attributes, their types and relationships. They roughly correspond to metadata in statistical disclosure control.

  3. 3.

    A description of hash functions , very common in data structures, can be found e.g. in [74].

  4. 4.

    The discussion of which are the quasi-identifiers for attacking a database is present in e.g. the literature on data protection for graphs and social networks. There are a few competing definitions of k-anonymity for graphs that correspond to different sets of quasi-identifiers. We will discuss them in Sect. 6.4.2 (on algorithms for k-anonymity for big data).

  5. 5.

    There are two main types of methods for anomaly detection: models based on misuses (a database of misuses is used to learn what an anomaly is) and models based on correct activity (the model we learn explains normal activity, and what diverges from the model is classified as an anomaly). The latter approach seems more suitable when data is protected if this process can eliminate outliers, and, thus, the anomalies of a database.

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    Vicenç Torra

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Torra, V. (2017). Privacy Models and Disclosure Risk Measures. In: Data Privacy: Foundations, New Developments and the Big Data Challenge. Studies in Big Data, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-57358-8_5

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