Privacy in the Smart City: Implications of Sensor Network Design, Law, and Policy for Locational Privacy

Abstract

Automated data collection technology has transformed the city. From sensors that measure traffic flow to scanners that help police officers locate stolen vehicles, the use of smart city sensor networks has become an integral part of the modern city. Because the sensor networks collect highly detailed information about every aspect of city life, data from the sensor networks is often linked to individuals and can potentially be used to develop detailed profiles on residents. This thesis defines and identifies threats to locational privacy and identifies three major approaches to mitigating privacy risk: the use of tiered data structures modeled on IRB reviews; the use of privacy by design principles to minimize privacy-adverse data collection; and amendments to policy and law that would help to create a regulatory environment favorable to individual and societal privacy protection. This thesis involves case study research and interviews with program managers at the City of Seattle Policy Department, the City of Seattle Department of Transportation, and Sound Transit. It considers the locational privacy implications of automated license plate readers, WiFi beacon sniffer networks, and RFID transit passes. It concludes that locational privacy protection will require a careful blend of policy, law, and system design that is tailored both to the technology and to the larger political and legal environment in which the sensor network operates.