  Data sharing between different organizations is an essential process in
today's connected world. However, recently there were many concerns about data
sharing as sharing sensitive information can jeopardize users' privacy. To
preserve the privacy, organizations use anonymization techniques to conceal
users' sensitive data. However, these techniques are vulnerable to
de-anonymization attacks which aim to identify individual records within a
dataset. In this paper, a two-tier mathematical framework is proposed for
analyzing and mitigating the de-anonymization attacks, by studying the
interactions between sharing organizations, data collector, and a prospective
attacker. In the first level, a game-theoretic model is proposed to enable
sharing organizations to optimally select their anonymization levels for
k-anonymization under two potential attacks: background-knowledge attack and
homogeneity attack. In the second level, a contract-theoretic model is proposed
to enable the data collector to optimally reward the organizations for their
data. The formulated problems are studied under single-time sharing and
repeated sharing scenarios. Different Nash equilibria for the proposed game and
the optimal solution of the contract-based problem are analytically derived for
both scenarios. Simulation results show that the organizations can optimally
select their anonymization levels, while the data collector can benefit from
incentivizing the organizations to share their data.
