NIST is closely monitoring guidance from Federal, State, and local health authorities on the outbreak of COVID-19. To protect the health and safety of NIST employees and the American public they continue to serve, NIST has decided to cancel the event. For more information on COVID-19, please visit: cdc.gov/covid19. For questions regarding registration, please contact Karen.Startsman [at] nist.gov (Karen[dot]Startsman[at]nist[dot]gov)
Privacy concerns surrounding Personally Identifiable Information (PII) limit the use of Public Safety data and present barriers to sharing data across agencies and systems in ways that could inform potentially life-saving strategic decisions and improve operational transparency with the public. As Public Safety moves towards leveraging advanced analytic technologies, the demand for sharing data sets to conduct research to address these needs will rise – necessitating the ability to properly de-identify data sets with efficient, robust, and quantitatively validated algorithms that ensure the protection of PII for both Public Safety personnel and the citizens they serve.
This workshop builds upon the 2018 NIST Public Safety Communications Research (PSCR) Differential Privacy Synthetic Data Challenge which was intended to stimulate novel research in the de-identification of data, raise awareness of differential privacy as a data privacy method, and invigorate the development of new approaches in differential privacy.
In this workshop, NIST aims to explore the interests and needs related to fundamental data privacy technology research by bringing together public safety data owners with data privacy technology experts, researchers, and public safety data users. The workshop will help NIST understand current technology approaches to data privacy risk management and needs in the Public Safety Community. It will explore further concepts in differential privacy, such as additional research areas and approaches, how to qualitatively describe and understand these technical concepts, as well as, methods to evaluate the usability and quality of the differentially private data. The discussions are intended to inform future research in this area at NIST.