Abstract
Over the last few years, the vast progress in genome sequencing has highly increased the availability of genomic data. Today, individuals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on personal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacy-sensitive and highly correlated between relatives. Therefore, individuals’ decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to protect and (ii) whether or not to reveal their genome. We study this tension by using a game-theoretic approach. First, we model the interplay between two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent influence diagrams that enable us to efficiently compute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing benefits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.
Erman Ayday—This work was carried out while the author was at EPFL.
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Notes
- 1.
- 2.
Each player takes into account the other players’ utility when making a decision.
- 3.
See, e.g., https://genomeprivacy.org/ for an introduction to genomics.
- 4.
See, e.g., http://opensnp.wordpress.com/2011/11/17/first-results-of-the-survey-on- sharing-genetic-information/ to understand users’ motivations for and fears about genome sharing.
- 5.
Note that an expected monetary loss would be expressed as a non-decreasing function of \(l_i\). This is left for future work.
- 6.
Note that a SNP value is encoded by the set \(\{0,1,2\}\) whose elements represent the number of minor alleles in the SNP.
- 7.
In \(G_d\), we assume that a player who does not share his SNPs will always invest in security. Note also that \(G_d\) is a special case deriving from \(G_s\).
- 8.
\(k=1\) for first-degree relatives such as parent, child, sibling; \(k=2\) for second-degree relatives such as grandparent, grandchild, uncle, aunt, niece, and so on.
- 9.
- 10.
In MAIDs, random variables are called chance variables.
- 11.
See the definition of a relevance graph in Definition 5.4 of [17].
- 12.
As in Sect. 4, LD is not used as we assume the same set \({\varOmega }\) of SNPs potentially shared by the players and targeted by the adversary.
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We would like to thank KĂ©vin Huguenin and Alexandra-Mihaela Olteanu for their helpful comments and feedback.
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Humbert, M., Ayday, E., Hubaux, JP., Telenti, A. (2015). On Non-cooperative Genomic Privacy. In: Böhme, R., Okamoto, T. (eds) Financial Cryptography and Data Security. FC 2015. Lecture Notes in Computer Science(), vol 8975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47854-7_24
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