Why patient data cannot be easily forgotten?

Ruolin Su, Xiao Liu, Sotirios A. Tsaftaris

Keywords: Privacy, Patient-wise Forgetting, Scrubbing, Learning

Abstract: Rights provisioned within data protection regulations, permit patients to request that knowledge about their information be eliminated by data holders. With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient's data within AI models. However, forgetting patients' information, is still an under-explored problem. In this paper, we study the influence of patient data on model performance and formulate two hypotheses for a patient's data: either they are common and similar to other patients or form edge cases, i.e. unique and rare cases. This shows that it is not possible to easily forget patient data. We propose a targeted forgetting approach to perform patient-wise forgetting. Extensive experiments on the benchmark ACDC dataset showcase the improved performance of the proposed targeted forgetting approach as opposed to a state-of-the-art method.

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Paper Type: both

Primary Subject Area: Application: Other

Secondary Subject Area: Application: Other

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