Abstract
The term “Big Data” is used to describe a universe of very large datasets that hold a variety of data types. This has spawned a new generation of information architectures and applications to facilitate the fast processing speeds and the visualization needed to analyze and extract value from these extremely large sets of data, using distributed platforms. While not all data in Big Data applications will be personally identifiable, when this is the case, privacy interests arise. To be clear, privacy requirements are not obstacles to innovation or to realizing societal benefits from Big Data analytics—in fact, they can actually foster innovation and doubly-enabling, win–win outcomes. This is achieved by taking a Privacy by Design approach to Big Data applications. This chapter begins by defining information privacy, then it will provide an overview of the privacy risks associated with Big Data applications. Finally, the authors will discuss Privacy by Design as an international framework for privacy, then provide guidance on using the Privacy by Design Framework and the 7 Foundational Principles, to achieve both innovation and privacy—not one at the expense of the other.
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Notes
- 1.
NIST (2015) defines ‘pseudonymization’ as a specific kind of transformation in which the names and other information that directly identifies an individual are replaced with pseudonyms. Pseudonymization allows linking information belonging to an individual across multiple data records or information systems, provided that all direct identifiers are systematically pseudonymized. Pseudonymization can be readily reversed if the entity that performed the pseudonymization retains a table linking the original identities to the pseudonyms, or if the substitution is performed using an algorithm for which the parameters are known or can be discovered.
- 2.
There are many government Open Data initiatives such as U.S. Government’s Open Data at www.data.gov; Canadian Government’s Open Data at http://open.canada.ca/en/open-data; UN Data at http://data.un.org/; EU Open Data Portal at https://data.europa.eu/euodp/en/data/. This is just a sample of the many Open Data sources around the world.
- 3.
In news media an echo chamber is a metaphorical description of a situation in which information, ideas, or beliefs are amplified or reinforced by transmission and repetition inside an “enclosed” system, where different or competing views are censored, disallowed, or otherwise underrepresented. The term is by analogy with an acoustic echo chamber, where sounds reverberate.
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Cavoukian, A., Chibba, M. (2018). Start with Privacy by Design in All Big Data Applications. In: Srinivasan, S. (eds) Guide to Big Data Applications. Studies in Big Data, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-53817-4_2
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