The Principle of Purpose Limitation and Big Data

Nikolaus Forgó (), Stefanie Hänold and Benjamin Schütze
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Nikolaus Forgó: Leibniz Universität Hannover
Stefanie Hänold: Leibniz Universität Hannover
Benjamin Schütze: Leibniz Universität Hannover

A chapter in New Technology, Big Data and the Law, 2017, pp 17-42 from Springer

Abstract: Abstract In recent years, Big Data has become a dominating trend in information technology. As a buzzword, Big Data refers to the analysis of large data sets in order to find new correlations—for example, to find business or political trends or to prevent crime—and to extract valuable information from large quantities of data. As much as Big Data may be useful for better decision-making and risk or cost reduction, it also creates some legal challenges. Especially where personal data is processed in Big Data applications such methods must be reconciled with data protection laws and principles. Those principles need some further analysis and refinement in the light of technical developments. Particularly challenging in that respect is the key principle of “purpose limitation.” It provides that personal data must be collected for specified, explicit and legitimate purposes and not further processed in a way incompatible with those purposes. This may be difficult to achieve in Big Data scenarios. At the time personal data is collected, it may still be unclear for what purpose it will later be used. However, the blunt statement that the data is collected for (any possible) Big Data analytics is not a sufficiently specified purpose. Therefore, this contribution seeks to offer a closer analysis of the principle of purpose limitation in European data protection law in the context of Big Data applications in order to reveal legal obstacles and lawful ways to handle such obstacles.

Keywords: Big Data; Purpose limitation; Purpose specification; Compatible use; Data protection; General data protection regulation (GDPR); Data protection directive (DPD) (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-981-10-5038-1_2

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