A new technology called SignalTrace is emerging as a tool for law enforcement, combining automated license plate reader (ALPR) data with wireless signals from personal devices. The system, developed by Leonardo, aims to create electronic fingerprints that can link recurring travel patterns of vehicles with the signals from phones, smartwatches, and other electronics. This raises significant privacy questions about how anonymous device signals remain when tied to specific locations, vehicles, and other devices.
SignalTrace works by detecting publicly broadcast electronic signals from devices. Its software identifies signals that repeatedly appear together, creating what the company terms an electronic fingerprint for a group. This data can then be correlated with license plate information and time-stamped locations. According to Leonardo, the technology can help investigators identify vehicles even when license plates are not visible, potentially offering a new starting point for investigations.
Leonardo emphasizes that SignalTrace does not decrypt communications, access message content, or retrieve data stored on devices. The company states that the system itself does not identify individuals. However, it acknowledges that investigators could potentially link an electronic signature to a license plate and then, using separate vehicle registration records and applicable laws, obtain information about the registered owner. This means any direct identification relies on external investigative steps and records, not solely on the SignalTrace data itself.
The privacy debate is amplified by the understanding that even seemingly anonymous data can become identifying when combined with other information. Studies have shown that a few points of location data can uniquely characterize an individual's movement patterns. When this location data is linked with vehicle records and repeated appearances over time, it can provide a more detailed picture for investigators.
Furthermore, SignalTrace's ability to track device proximity raises concerns about associations. Recurring patterns of devices traveling together could reveal relationships between individuals. Researchers have demonstrated that proximity data can accurately classify social relationships, though SignalTrace's specific capabilities in this regard have not been independently verified.
Leonardo reports that SignalTrace is in the early stages of adoption with a limited number of deployments. The company notes growing interest beyond law enforcement, including potential applications in parking management and traffic monitoring.
The legal landscape surrounding such technologies is evolving. The Supreme Court's 2018 ruling in Carpenter v. United States established a reasonable expectation of privacy in historical cellphone location records. More recently, in Chatrie v. United States, the Court held that obtaining geofence data from Google constituted a Fourth Amendment search. While Chatrie involved historical data held by a third party and SignalTrace collects broadcast signals, these cases highlight the increasing scrutiny of location data and digital privacy.
Privacy advocates point to scenarios like protests or public gatherings where SignalTrace sensors might be operating. A device repeatedly appearing near those associated with individuals under investigation could become an investigative lead, even if proximity does not imply knowledge or wrongdoing. The company maintains that SignalTrace data is an investigative lead, not proof of identity, and that any conclusions require additional corroborating information.
To mitigate some digital exposure, users can turn off Bluetooth when not in use, review app permissions for location access, and research how license plate readers are used in their communities. However, no single phone setting can guarantee complete invisibility to all roadside sensors.
The evolving definition of identification is central to the debate. While SignalTrace may not directly capture a person's name, the patterns it creates, when combined with other data sources, can contribute to identifying individuals. The key question remains how law enforcement will use this data, what level of corroboration is required, and what legal protections will be afforded to individuals as such technologies become more widespread.