Search

Biomedical research has become data-driven. To create the required big datasets, health data needs to be shared or reused out of the context of its initial purpose. This leads to significant privacy challenges. Data anonymization is an important protection method where data is transformed such that privacy guarantees can be provided according to formal models. For applications in practice, anonymization methods need to be integrated into scalable and robust tools. In this work, we focus on the problem of scalability.Protecting biomedical data from inference attacks is challenging, in particular for numeric data. An important privacy model in this context is t-closeness, which has also been defined for attribute values which are totally ordered. However, directly implementing a scalable algorithmic representation of the mathematical definition of the model proves difficult. In this paper we therefore present a series of optimizations that can be used to achieve efficiency in production use. An experimental evaluation shows that our approach reduces execution times of anonymization processes involving t-closeness by up to a factor of two. [ABSTRACT FROM AUTHOR]

When individual-level health data are shared in biomedical research, the privacy of patients must be protected. This is typically achieved by data de-identification methods, which transform data in such a way that formal privacy requirements are met. In the process, it is important to minimize the loss of information to maintain data quality. Although several models have been proposed for measuring this aspect, it remains unclear which model is best suited for which application. We have therefore performed an extensive experimental comparison. We first implemented several common quality models into the ARX de-identification tool for biomedical data. We then used each model to de-identify a patient discharge dataset covering almost 4 million cases and outputs were analyzed to measure the impact of different quality models on real-world applications. Our results show that different models are best suited for specific applications, but that one model (Non-Uniform Entropy) is particularly well suited for general-purpose use.