PrivaScope Use Cases


WiFi Data Requests

University faculty told ITS that WiFi location data would be of value to learning analytics, mobility, and social research.

As demand for location data has grown, so to have privacy concerns. This has caused the University Privacy Officer to publish Your WiFi Location Data privacy page, as well as develop training for staff with access to the data. 

ITS offers faculty, students, and staff access to their own personal WiFi data, as generated by the event-driven Radius infrastructure. Individuals can download their own data as a .CSV file and share it as desired. This individual-sharing infrastructure was requested by MIDAS-awardee Professor Rada Mihalcea, and is deemed acceptable per the Your WiFi Location Data privacy page.

Note: If you have any concerns or questions about how your personal information is collected or used, please contact the U-M Privacy Office at [email protected].



Professors Pascal Van Hentenryck and Huei Peng are both interested in understanding the patterns of wide-scale movement of individuals across campus in hopes of re-architecting the university’s transportation infrastructure.

Reinventing Urban Transportation and Mobility (RITMO) project researchers are actively collaborating with the development and refinement of the PrivaScope infrastructure to achieve their goals of identifying mobility patterns of arrivals and departures on Central, North, and South campus.

Learning Analytics

Professors Rada Mihalcea and Perry Samson will be using PrivaScope for their research on the impact of student behaviors (e.g., going to the gym or attending class regularly) on grade performance.

Research Collaboration

Professor Jason Owen-Smith has conducted past research on the impact of close physical proximity on research collaborations. With the use of PrivaScope, this research can be taken further to understand the potential impact of university scientists' incidental path intersections (collisions) on obtaining cross-disciplinary research grants.


PrivaScope infrastructure, its timeline data collection, and its ability to protect privacy make it an ideal tool to gain insight on how campus services and resources are used. For example, PrivaScope data might assist with the placement of campus food service locations or to understand the utilization of campus buildings and rooms.


If the need arises to present this data in a form that could be integrated with other universities’ data in accordance with the Unizin effort, we will examine this request.