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Title: Privacy and Confidentiality in Service Science and Big Data Analytics

Citation Type: Book, Section

Publication Year: 2014

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Abstract: Vast amounts of data are now being collected from census and surveys, scientific research, instruments, observation of consumer and internet activities, and sensors of many kinds. These data hold a wealth of information, however there is a risk that personal privacy will not be protected when they are accessed and used. This paper provides an overview of current and emerging approaches to balancing use and analysis of data with confidentiality protection in the research use of data, where the need for privacy protection is widely-recognised. These approaches were generally developed in the context of national statistical agencies and other data custodians releasing social and survey data for research, but are increasingly being adapted in the context of the globalisation of our information society. As examples, the paper contributes to a discussion of some of the issues regarding confidentiality in the service science and big data analytics contexts.

Url: https://link.springer.com/chapter/10.1007/978-3-319-18621-4_5

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Authors: O’Keefe, Christine, M

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Pages: 54-70

Volume Title: IFIP International Summer School on Privacy and Identity Management

Publisher: Springer

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Data Collections: IPUMS International

Topics: Population Data Science

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