Abstract: Methods, systems, and devices supporting physical proximity graphing are described. Some systems may store a limited set of location information for a set of humans (e.g., refraining from tracking device location information to maintain user privacy). A system may send a survey to each person indicating a suggested list of people based on the location information. The people may provide feedback confirming whether encounters occurred with each suggested human. The system may use this information to generate a graph using hashed identifiers to obfuscate user identities, where the graph tracks connections between people (e.g., based on encounters within a time period). Based on a trigger event, the system may analyze the graph and identify a set of hashed identifiers connected to a person of interest (e.g., traversing multiple levels of the graph). The system may transmit messages to people corresponding to the identified set of hashed identifiers.

Abstract: Methods, systems, and devices supporting physical proximity graphing are described. Some systems may store a limited set of location information for a set of humans (e.g., refraining from tracking device location information to maintain user privacy). A system may send a survey to each person indicating a suggested list of people based on the location information. The people may provide feedback confirming whether encounters occurred with each suggested human. The system may use this information to generate a graph using hashed identifiers to obfuscate user identities, where the graph tracks connections between people (e.g., based on encounters within a time period). Based on a trigger event, the system may analyze the graph and identify a set of hashed identifiers connected to a person of interest (e.g., traversing multiple levels of the graph). The system may transmit messages to people corresponding to the identified set of hashed identifiers.