Humbert, MathiasDupertuis, DidierCherubini, MauroHuguenin, Kevin2022-09-262022-09-262022-09-262022-01-0110.1109/EuroSP53844.2022.00033https://infoscience.epfl.ch/handle/20.500.14299/190975WOS:000851574500025Direct-to-consumer genetic testing services are gaining momentum: As of today, companies such as 23andMe or AncestryDNA have already attracted 26 million customers. These services raise privacy concerns, exacerbated by the fact that their customers can then share their genomic data on platforms such as GEDmatch. Notwithstanding their right to learn about their genetic background or to share their genomic data, it is paramount that individuals realize that such a behavior damages their relatives' privacy (i.e., kin genomic privacy). In this paper, we present KGP Meter, a new online tool that provides means for raising awareness in the general public about the privacy risks of genomic data sharing. Our tool features various properties that makes it highly interactive, privacy-preserving (i.e., not requiring access to the actual genomic data), and user-friendly. It explores possible configurations in an optimized way and combines well-established graphical models with an entropy-based metric to compute kin genomic privacy scores. Our experiments show that KGP Meter is very responsive. We design and implement an interface that enables users to draw their family trees and indicate which of their relatives' genomes are known, and that communicates the resulting privacy scores to the users. We then analyze the usage of the tool and survey users to better understand users' perceptions towards these risks and evaluate our tool. We observe that most of them find the privacy score worrisome, and that the large majority of them find KGP Meter useful.Computer Science, Information SystemsComputer Science, Interdisciplinary ApplicationsComputer Science, Theory & MethodsComputer Sciencetext::conference output::conference proceedings::conference paper