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Quietly making noise: Measuring differential privacy could balance meaningful analytics and identity protection

To balance personal safety and research innovation, researchers at ORNL are employing a mathematical technique known as differential privacy to provide data privacy guarantees.

The Linac Coherent Light Source at DOE’s SLAC National Accelerator Laboratory in California reveals the structural dynamics of atoms and molecules through X-ray snapshots at ultrafast timescales. Pictured here is the LCLS-II tunnel. Credit: Jim Gensheimer/SLAC National Accelerator Laboratory

Plans to unite the capabilities of two cutting-edge technological facilities funded by the Department of Energy’s Office of Science promise to usher in a new era of dynamic structural biology. Through DOE’s Integrated Research Infrastructure, or IRI, initiative, the facilities will complement each other’s technologies in the pursuit of science despite being nearly 2,500 miles apart.

The AI for Energy Report provides a framework for using AI to accelerate decarbonization of the U.S. economy. Credit: Argonne National Laboratory

Groundbreaking report provides ambitious framework for accelerating clean energy deployment while minimizing risks and costs in the face of climate change.

The transportation and industrial sectors together account for more than 50% of the country’s carbon footprint. Defossilization could help reduce new emissions from these and other difficult-to-electrify segments of the U.S. economy.

Scientists at Oak Ridge National Laboratory and six other Department of Energy national laboratories have developed a United States-based perspective for achieving net-zero carbon emissions. 

SOS26 attendees standing in front of the Kennedy Space Center on Merrit Island, Florida the night of their dinner reception provided by the conference sponsors. The keynote speaker was Rupak Biswas from NASA. Credit: Judy Potok/ORNL, U.S. Dept. of Energy

Held in Cocoa Beach, Florida from March 11 to 14, researchers across the computing and data spectra participated in sessions developed by staff members from the Department of Energy’s Oak Ridge National Laboratory, or ORNL, Sandia National Laboratories and the Swiss National Supercomputing Centre. 

ORNL’s Suhas Sreehari explains the algebraic and topological foundations of representation systems, used in generative AI technology such as large language models. Credit: Lena Shoemaker/ORNL, U.S. Dept. of Energy

In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user. 
 

Credit: Tyler Spano/ORNL, U.S. Dept. of Energy

Nuclear nonproliferation scientists at ORNL have published the Compendium of Uranium Raman and Infrared Experimental Spectra, a public database and analysis of structure-spectral relationships for uranium minerals. This first-of-its-kind dataset and corresponding analysis fill a key gap in the existing body of knowledge for mineralogists and actinide scientists. 

Assaf Anyamba Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

ORNL’s Assaf Anyamba has spent his career using satellite images to determine where extreme weather may lead to vector-borne disease outbreaks. His work has helped the U.S. government better prepare for outbreaks that happen during periods of extended weather events such as El Niño and La Niña, climate patterns in the Pacific Ocean that can affect weather worldwide. 

ORNL

ORNL took home the top honors in three categories at the second annual DOE Geospatial Science Poster competition, held on National GIS Day. For the second year in a row, DOE awarded ORNL top prize as Best Geospatial Program. Additionally, ORNL geospatial researchers took home first place prizes for their posters in the Best Departmental Element Alignment and Best Cartography categories.

ORNL researchers developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with more accurate information on cancer reporting.

In partnership with the National Cancer Institute, researchers from ORNL and Louisiana State University developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with exponentially more accurate information on cancer reporting.