# De-anonymization Attacks on Neuroimaging Datasets

## TL;DR

This paper demonstrates a de-anonymization attack on neuroimaging datasets that exploits unique brain features to identify individuals and their task performance, raising privacy concerns.

## Contribution

It introduces a novel, scalable matrix analysis-based attack method that can accurately de-anonymize neuroimages and reveal sensitive information.

## Key findings

- The attack accurately identifies individuals across datasets.
- It can also infer the tasks performed by subjects.
- The method is computationally efficient and scalable.

## Abstract

Advances in imaging technologies, combined with inexpensive storage, have led to an explosion in the volume of publicly available neuroimaging datasets. Effective analyses of these images hold the potential for uncovering mechanisms that govern functioning of the human brain, and understanding various neurological diseases and disorders. The potential significance of these studies notwithstanding, a growing concern relates to the protection of privacy and confidentiality of subjects who participate in these studies. In this paper, we present a de-anonymization attack rooted in the innate uniqueness of the structure and function of the human brain. We show that the attack reveals not only the identity of an individual, but also the task they are performing, and their efficacy in performing the tasks. Our attack relies on novel matrix analyses techniques that are used to extract discriminating features in neuroimages. These features correspond to individual-specific signatures that can be matched across datasets to yield highly accurate identification. We present data preprocessing, signature extraction, and matching techniques that are computationally inexpensive, and can scale to large datasets. We discuss implications of the attack and challenges associated with defending against such attacks.