Abstract
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., OpenSNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer’s or Parkinson’s), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person’s genome is correlated to his family members’ genomes, thus leading to interdependent privacy risks. In this work, focusing on our existing and ongoing work on genomic privacy, we will first highlight one serious threat for genomic privacy. Then, we will present the high level descriptions of our cryptographic solutions to protect the privacy of genomic data.
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- 1.
SNPs carry privacy-sensitive information about individuals’ health. Recent discoveries show that the susceptibility of an individual to several diseases can be computed from his SNPs.
- 2.
The exact sequence of the family members (whose SNPs are revealed) is indicated for each evaluation.
- 3.
In this study, we only focus on the diseases which can be analyzed using the SNPs. We admit that there are also other diseases which depend on other forms of mutations or environmental factors.
- 4.
Depending on the privacy-sensitivity of the clinical and environmental data, the patient can choose which clinical and environmental attributes to reveal to the MU, and which ones to encrypt and keep at the SPU.
- 5.
A patient can choose a low-entropy password that is easier for him/her to remember, which is a common case in the real world [8].
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© 2016 International Financial Cryptography Association
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Ayday, E. (2016). Cryptographic Solutions for Genomic Privacy. In: Clark, J., Meiklejohn, S., Ryan, P., Wallach, D., Brenner, M., Rohloff, K. (eds) Financial Cryptography and Data Security. FC 2016. Lecture Notes in Computer Science(), vol 9604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53357-4_22
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DOI: https://doi.org/10.1007/978-3-662-53357-4_22
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