  Data mining is an increasingly important technology for extracting useful
knowledge hidden in large collections of data. There are, however, negative
social perceptions about data mining, among which potential privacy violation
and potential discrimination. Automated data collection and data mining
techniques such as classification have paved the way to making automated
decisions, like loan granting/denial, insurance premium computation. If the
training datasets are biased in what regards discriminatory attributes like
gender, race, religion, discriminatory decisions may ensue. In the first part
of this thesis, we tackle discrimination prevention in data mining and propose
new techniques applicable for direct or indirect discrimination prevention
individually or both at the same time. We discuss how to clean training
datasets and outsourced datasets in such a way that direct and/or indirect
discriminatory decision rules are converted to legitimate (non-discriminatory)
classification rules. In the second part of this thesis, we argue that privacy
and discrimination risks should be tackled together. We explore the
relationship between privacy preserving data mining and discrimination
prevention in data mining to design holistic approaches capable of addressing
both threats simultaneously during the knowledge discovery process. As part of
this effort, we have investigated for the first time the problem of
discrimination and privacy aware frequent pattern discovery, i.e. the
sanitization of the collection of patterns mined from a transaction database in
such a way that neither privacy-violating nor discriminatory inferences can be
inferred on the released patterns. Moreover, we investigate the problem of
discrimination and privacy aware data publishing, i.e. transforming the data,
instead of patterns, in order to simultaneously fulfill privacy preservation
and discrimination prevention.
