Visible to the public Identifying Personal DNA Methylation Profiles by Genotype Inference

TitleIdentifying Personal DNA Methylation Profiles by Genotype Inference
Publication TypeConference Paper
Year of Publication2017
AuthorsBackes, M., Berrang, P., Bieg, M., Eils, R., Herrmann, C., Humbert, M., Lehmann, I.
Conference Name2017 IEEE Symposium on Security and Privacy (SP)
ISBN Number978-1-5090-5533-3
KeywordsBioinformatics, biomedical research community, Cancer, cryptographic scheme, cryptography, data privacy, DNA, DNA cryptography, DNA methylation data, epigenetic element, genomic data, genomics, genotype inference, Health Care, Human Behavior, human health, inference mechanisms, Law, medical computing, Metrics, personal DNA methylation profiles, personalized medicine, predictive medicine, privacy, privacy-sensitive data, pubcrawl, Resiliency, whole-genome sequencing

Since the first whole-genome sequencing, the biomedical research community has made significant steps towards a more precise, predictive and personalized medicine. Genomic data is nowadays widely considered privacy-sensitive and consequently protected by strict regulations and released only after careful consideration. Various additional types of biomedical data, however, are not shielded by any dedicated legal means and consequently disseminated much less thoughtfully. This in particular holds true for DNA methylation data as one of the most important and well-understood epigenetic element influencing human health. In this paper, we show that, in contrast to the aforementioned belief, releasing one's DNA methylation data causes privacy issues akin to releasing one's actual genome. We show that already a small subset of methylation regions influenced by genomic variants are sufficient to infer parts of someone's genome, and to further map this DNA methylation profile to the corresponding genome. Notably, we show that such re-identification is possible with 97.5% accuracy, relying on a dataset of more than 2500 genomes, and that we can reject all wrongly matched genomes using an appropriate statistical test. We provide means for countering this threat by proposing a novel cryptographic scheme for privately classifying tumors that enables a privacy-respecting medical diagnosis in a common clinical setting. The scheme relies on a combination of random forests and homomorphic encryption, and it is proven secure in the honest-but-curious model. We evaluate this scheme on real DNA methylation data, and show that we can keep the computational overhead to acceptable values for our application scenario.

Citation Keybackes_identifying_2017