  Mining health data can lead to faster medical decisions, improvement in the
quality of treatment, disease prevention, reduced cost, and it drives
innovative solutions within the healthcare sector. However, health data is
highly sensitive and subject to regulations such as the General Data Protection
Regulation (GDPR), which aims to ensure patient's privacy. Anonymization or
removal of patient identifiable information, though the most conventional way,
is the first important step to adhere to the regulations and incorporate
privacy concerns. In this paper, we review the existing anonymization
techniques and their applicability to various types (relational and
graph-based) of health data. Besides, we provide an overview of possible
attacks on anonymized data. We illustrate via a reconstruction attack that
anonymization though necessary, is not sufficient to address patient privacy
and discuss methods for protecting against such attacks. Finally, we discuss
tools that can be used to achieve anonymization.
