A utility maximizing and privacy preserving approach for protecting kinship in genomic databases. Motivation: Rapid and low cost sequencing of genomes enabled widespread use of genomic data in research studies and personalized customer applications, where genomic data is shared in public databases. Although the identities of the participants are anonymized in these databases, sensitive information about individuals can still be inferred. One such information is kinship. Results: We define two routes kinship privacy can leak and propose a technique to protect kinship privacy against these risks while maximizing the utility of shared data. The method involves systematic identification of minimal portions of genomic data to mask as new participants are added to the database. Choosing the proper positions to hide is cast as an optimization problem in which the number of positions to mask is minimized subject to privacy constraints that ensure the familial relationships are not revealed. We evaluate the proposed technique on real genomic data. Results indicate that concurrent sharing of data pertaining to a parent and an offspring results in high risks of kinship privacy, whereas the sharing data from further relatives together is often safer. We also show arrival order of family members have a high impact on the level of privacy risks and on the utility of sharing data. Availability and implementation: https://github.com/tastanlab/Kinship-PrivacyA utility maximizing and privacy preserving approach for protecting kinship in genomic databases.
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- Ünal, Ali Burak; Akgün, Mete; Pfeifer, Nico: A framework with randomized encoding for a fast privacy preserving calculation of non-linear kernels for machine learning applications in precision medicine (2019)