A game theoretic approach to balance privacy risks and familial benefits.

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users' valuations of the service will affect a population's behavior.

Matching Soulmates.

We study iterated matching of soulmates [IMS], a recursive process of forming coalitions that are mutually preferred by members to other coalition containing individuals as yet unmatched by this process. If all players can be matched this way, preferences are IMS-complete. A mechanism is a soulmate mechanism if it allows the formation of all soulmate coalitions.

Using game theory to thwart multistage privacy intrusions when sharing data.

Person-specific biomedical data are now widely collected, but its sharing raises privacy concerns, specifically about the re-identification of seemingly anonymous records. Formal re-identification risk assessment frameworks can inform decisions about whether and how to share data; current techniques, however, focus on scenarios where the data recipients use only one resource for re-identification purposes. This is a concern because recent attacks show that adversaries can access multiple resources, combining them in a stage-wise manner, to enhance the chance of an attack’s success.