Funds from this grant support efforts to integrate the various diverse research projects and initiatives of the Deep Carbon Observatory (DCO) through the development of new numerical models and visualizations.  Geologist Elizabeth Cottrell of the Smithsonian Institution’s National Museum of Natural History will lead a multidisciplinary team of researchers, technologists, and representatives from each of the DCO’s four scientific directorates to spearhead the collaborative development of new models and visualizations that incorporate the data collected and theoretical insights developed by DCO researchers in the field.  Contrell and her team will convene a workshop of DCO stakeholders to set modeling and visualization priorities, create an introductory visualization of the history of terrestrial vulcanism, and oversee the distribution of a small number of seed grants to stimulate modeling work on projects identified as high priority.  The project promises several benefits, including forcing consistency upon diverse DCO research efforts, revealing gaps in measurement and functional understanding within the DCO community, and spurring new insights and projections. 

This grant funds work by Alice Silverberg of the University of California, Irvine to bring together mathematicians, cryptologists, and computer scientists in a concerted research effort to build on recent breakthroughs in fully homomorphic encryption (FHE).  FHE is a promising new encryption technique that allows accurate computation on encrypted data, allowing third parties to perform calculations on datasets without the need to decrypt them first. Recent mathematical breakthroughs in FHE have shown it to be theoretically possible, though extant techniques are too slow and unwieldy for practical use.  Silverstein’s project will bring attention and intellectual firepower to the issue, in the hopes of eventually crafting more feasible FHE approaches, with consequent benefits for the conduct of privacy-preserving research by allowing scientific analysis of private, proprietary, and otherwise sensitive data.