Database Design Protects Citizen Privacy in Smart City Applications
Private citizens can know that their activities will remain private as cities gather smart-city data into a database developed by researchers at the Center for Smart Streetscapes (CS3), an NSF-funded Engineering Research Center (ERC) based at Columbia University.
Emerging technologies pose new privacy, security, and fairness challenges that may compromise social equity—including the data that governments will gather to support smart city applications. CS3 researchers developed a differentially-private time-series database, which uses algorithms to automatically generate “synthetic data” that describes the patterns within the group while withholding information about specific individuals.
As governments gather data from public spaces to support smart city applications, the risk of privacy disclosures grows fast. An example is a city that gathers data for insights into how the walking public moves throughout their downtowns. Tracking pedestrian movements can obviously compromise the privacy of individuals, a risk that grows as cities add other applications to improve traffic, safety, and parking, among other goals.
Traditional general programming frameworks do not analyze the compounding risks of sharing data streams across smart city applications. The CS3 database incorporates privacy by design and is the first component of a programming framework to ensure the development of other secure, private, and fair smart city applications.
