Biomedical data is widely collected in the field of medicine, although sharing such data can raise privacy concerns about the re-identification of seemingly anonymous records. Risk assessment frameworks for formal re-identification can inform decisions on the process of sharing data, and current methods focus on scenarios where data recipients use only one resource to identify purposes. However, this can affect privacy where adversaries can access multiple resources to enhance the chance of their success. In a new report now in Science Advances, Zhiyu Wan and a team of scientists in electrical engineering and computer engineering and biomedical informatics in the U.S. represented a re-identification game using a two-player Stackelberg game of perfect information to assess risk. They suggest an optimal data-sharing strategy based on a privacy-utility trade-off. The team used experiments with large-scale genomic datasets and game theoretic models to induce adversarial capabilities to effectively share data with low re-identification risk.
Click here for original story, Using game theory to thwart multistage privacy intrusions when sharing data
Source: Phys.org