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P. Kieseberg, S. Schrittwieser, M. Mulazzani, I. Echizen, E. Weippl:
"An algorithm for collusion-resistant anonymization and fingerprinting of sensitive microdata";
Electronic Markets, 24 (2014), 2; 113 - 124.

The collection, processing, and selling of personal
data is an integral part of today´s electronic markets, either as
means for operating business, or as an asset itself. However,
the exchange of sensitive information between companies is
limited by two major issues: Firstly, regulatory compliance
with laws such as SOX requires anonymization of personal
data prior to transmission to other parties. Secondly, transmission
always implicates some loss of control over the data since
further dissemination is possible without knowledge of the
owner. In this paper, we extend an approach based on the
utilization of k-anonymity that aims at solving both concerns
in one single step - anonymization and fingerprinting of
microdata such as database records. Furthermore, we develop
criteria to achieve detectability of colluding attackers, as well
as an anonymization strategy that resists combined efforts of
colluding attackers on reducing the anonymization-level.
Based on these results we propose an algorithm for the generation
of collusion-resistant fingerprints for microdata.

The collection, processing, and selling of personal
data is an integral part of today´s electronic markets, either as
means for operating business, or as an asset itself. However,
the exchange of sensitive information between companies is
limited by two major issues: Firstly, regulatory compliance
with laws such as SOX requires anonymization of personal
data prior to transmission to other parties. Secondly, transmission
always implicates some loss of control over the data since
further dissemination is possible without knowledge of the
owner. In this paper, we extend an approach based on the
utilization of k-anonymity that aims at solving both concerns
in one single step - anonymization and fingerprinting of
microdata such as database records. Furthermore, we develop
criteria to achieve detectability of colluding attackers, as well
as an anonymization strategy that resists combined efforts of
colluding attackers on reducing the anonymization-level.
Based on these results we propose an algorithm for the generation
of collusion-resistant fingerprints for microdata.