Database Reference
In-Depth Information
In Chapter 10, Van den Braak, Choenni and Verwer discuss the challenges
concerning combining and analyzing judicial databases. Several organizations in
the criminal justice system collect and process data on crime and law enforcement.
Combining and analyzing data from different organizations may be very useful,
for instance, for security policies. Two approaches are discussed, a data warehouse
(particularly useful on an individual level) and a dataspace approach (particularly
useful on an aggregated level). Though in principle all applications exploiting
judicial data may violate data protection legislation, Van den Braak, Choenni and
Verwer show that a dataspace approach is preferable with regard to taking
precautions against such data protection legislation violations.
1.4.4 Part IV: Solutions in Code
Part IV of this topic provides technological solutions to the discrimination and
privacy issues discussed in Part II.
In Chapter 11, Matwin provides a survey of privacy preserving data mining
techniques and discusses the forthcoming challenges and the questions awaiting
solutions. Starting with protection of the data, methods for identity disclosure and
attribute disclosure are discussed. However, adequate protection of the data in
databases may not be sufficient: privacy infringements may also occur based on
the inferred data mining results. Therefore, also model based identity disclosure
methods are discussed. Furthermore, methods for sharing data for data mining
purposes while protecting the privacy of people who contributed the data are
discussed. Specifically, the chapter presents scenarios in which data is shared
between a number of parties, either in a horizontal a or vertical partition. Then the
privacy of individuals who contributed the data is protected by special-purpose
cryptographic techniques that allow parties performing meaningful computation
on the encrypted data. Finally, Matwin discusses new challenges like data from
mobile devices, data from social networks and cloud computing.
In Chapter 12, Kamiran, Calders and Pechenizkiy survey different techniques
for discrimination-free predictive models. Three types of techniques are discussed.
First, removing discrimination from the dataset before applying data mining tools.
Second, changing the learning procedures by restricting the search space to
models that are not discriminating. Third, adjusting the models learned by the data
mining tools after the data mining process. These techniques may significantly
reduce discrimination at the cost of accuracy. The authors' experiments show that
still very accurate models can be learned. Hence, the techniques presented by
Kamiran, Calders and Pechenizkiy provide additional opportunities for
policymakers to balance discrimination against accuracy.
In Chapter 13, Hajian and Domingo-Ferrer address the prevention of
discrimination that may result from data mining and profiling. Discrimination
prevention consists of inducing patterns that do not lead to discriminatory
decision, even if the original data in the database is inherently biased. A taxonomy
is presented for classifying and examining discrimination prevention methods.
Next, preprocessing discrimination prevention methods are introduced and it is
discussed how these methods deal with direct and indirect discrimination
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