From Data Minimization to Data Minimummization - Discrimination and Privacy in the Information Society - page 275

and organization of the data should be incorporated. With regard to analyzing data,
metadata should be incorporated about the process of analyses, the algorithms used,
the databases harvested and the methodology of mining. Finally, with regard to us-
ing (aggregated) data, data must be gathered about in what context the patterns,
profiles and rules will be applied and used.
By requiring and clustering a minimum set of (contextual) information, the val-
ue of the dataset is retained or even increased, and the privacy and discrimination
problems following from the loss of context might be better addressed than by the
data minimization principle. Nevertheless, it has to be stressed that not all privacy
and discrimination problems are caused by a loss of contextuality, nor can all pri-
vacy and discrimination problems be solved by the data mini mum mization prin-
ciples. Moreover, the data mini mum mization principles are neither totally new to
the technical, nor to the juridical doctrine. Finally, no efforts have been made in
this chapter to outline how data mini mum mization principles may be put into prac-
tice or be implemented in data mining rules.
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