HaploHide: A Data Hiding Framework for Privacy Enhanced Sharing of Personal Genetic Data

Personal genetic data is becoming a digital commodity as millions of individuals have direct access to and control of their genetic information. This information must be protected as it can be used for reidentification and potential discrimination of individuals and relatives. While there is a great incentive to share and use genetic information, there are limited number of practical approaches for protecting it when individuals would like to make use of their genomes in clinical and recreational settings. To enable privacy-enhanced usage of genomic data by individuals, we propose a crowd-blending-based framework where portions of the individuals haplotype is \"hidden\" within a large sample of other haplotypes. The hiding framework is motivated by the existence of large-scale population panels that we utilize for generation of the crowd of haplotypes in which the individuals haplotype is hidden. We demonstrate the usage of hiding in two different scenarios: Sharing of variant alleles on genes and sharing of GWAS variant alleles. We evaluate hiding framework by testing reidentification of hidden individuals using numerous measures of individual reidentification. In these settings, we discuss how effective hiding can be accomplished when the adversary does not have access to auxiliary identifying information. Compared to the existing approaches for protecting privacy, which require substantial changes in the computational infrastructure, e.g., homomorphic encryption, hiding-based framework does not incur any changes to the infrastructure. However, the processing must be performed for every sample in the crowd and therefore data processing cost will increase as the crowd size increases.