Synonyms
Individual data
Definition
A microdata file V with s respondents and t attributes is an s × t matrix where Vij is the value of attribute j for respondent i. Attributes can be numerical (e.g., age, salary) or categorical (e.g., gender, job).
Key Points
The attributes in a microdata set can be classified in four categories which are not necessarily disjoint [1, 2]:
- 1.
Identifiers. These are attributes that unambiguously identify the respondent. Examples are the passport number, social security number, name-surname, etc.
- 2.
Quasi-identifiers or key attributes. These are attributes which identify the respondent with some degree of ambiguity. (Nonetheless, a combination of quasi-identifiers may provide unambiguous identification.) Examples are address, gender, age, telephone number, etc.
- 3.
Confidential outcome attributes. These are attributes which contain sensitive information on the respondent. Examples are salary, religion, political affiliation, health condition, etc.
- 4.
Non-c...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Dalenius T. The invasion of privacy problem and statistics production: an overview. Statistik Tidskrift. 1974;12:213–25.
Samarati P. Protecting respondents’; identities in microdata release. IEEE Trans Knowl Data Eng. 2001;13(6):1010–27.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Domingo-Ferrer, J. (2018). Microdata. 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_1494
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1494
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering