It's all in the timing: calibrating temporal penalties for biomedical data sharing.

Objective: Biomedical science is driven by datasets that are being accumulated at an unprecedented rate, with ever-growing volume and richness. There are various initiatives to make these datasets more widely available to recipients who sign Data Use Certificate agreements, whereby penalties are levied for violations. A particularly popular penalty is the temporary revocation, often for several months, of the recipient's data usage rights. This policy is based on the assumption that the value of biomedical research data depreciates significantly over time; however, no studies have been performed to substantiate this belief. This study investigates whether this assumption holds true and the data science policy implications. Methods: This study tests the hypothesis that the value of data for scientific investigators, in terms of the impact of the publications based on the data, decreases over time. The hypothesis is tested formally through a mixed linear effects model using approximately 1200 publications between 2007 and 2013 that used datasets from the Database of Genotypes and Phenotypes, a data-sharing initiative of the National Institutes of Health. Results: The analysis shows that the impact factors for publications based on Database of Genotypes and Phenotypes datasets depreciate in a statistically significant manner. However, we further discover that the depreciation rate is slow, only ∼10% per year, on average. Conclusion: The enduring value of data for subsequent studies implies that revoking usage for short periods of time may not sufficiently deter those who would violate Data Use Certificate agreements and that alternative penalty mechanisms may need to be invoked.

An Open Source Tool for Game Theoretic Health Data De-Identification.

Biomedical data continues to grow in quantity and quality, creating new opportunities for research and data-driven applications. To realize these activities at scale, data must be shared beyond its initial point of collection. To maintain privacy, healthcare organizations often de-identify data, but they assume worst-case adversaries, inducing high levels of data corruption. Recently, game theory has been proposed to account for the incentives of data publishers and recipients (who attempt to re-identify patients), but this perspective has been more hypothetical than practical. In this paper, we report on a new game theoretic data publication strategy and its integration into the open source software ARX. We evaluate our implementation with an analysis on the relationship between data transformation, utility, and efficiency for over 30,000 demographic records drawn from the U.S. Census Bureau. The results indicate that our implementation is scalable and can be combined with various data privacy risk and quality measures.