SignalTrace, a new technology by Leonardo, links wireless device signals to license plate data, raising significant privacy concerns regarding how personal information may be inferred from electronic patterns.
SignalTrace, developed by the security company Leonardo, is a groundbreaking technology that detects wireless signals from consumer devices and associates them with license plate data, effectively creating electronic fingerprints for law enforcement use.
Imagine you regularly carpool to work with a colleague. As your vehicle passes a license plate reader, it captures the plate number along with the time and location. Simultaneously, a roadside sensor picks up wireless signals emitted by devices traveling nearby—your smartphone, a smartwatch, or even the car’s built-in electronics. Over time, the software can identify patterns, revealing that certain signals frequently travel together. This creates a recurring electronic pattern linked to that vehicle.
Weeks later, if one of those signals is detected in a different location, perhaps near another vehicle involved in an investigation, police may not initially know who owns the device. However, the historical connections could provide a valuable starting point for their inquiries.
The implications of SignalTrace are significant, particularly regarding privacy. Once a device signal can be linked to a vehicle and its travel patterns, how anonymous does that signal remain?
SignalTrace operates in conjunction with automatic license plate reader (ALPR) technology. Traditional ALPR systems photograph vehicles, recording their license plate numbers, vehicle details, and the time and location of each sighting. SignalTrace enhances this capability by detecting electronic signals from consumer devices, which can include smartphones, wearables, and RFID devices. The software identifies signals that frequently appear together, creating what Leonardo refers to as an electronic fingerprint for that group.
According to the company, SignalTrace can connect recurring device patterns with license plate information and time-stamped locations, allowing investigators to search these patterns later. Notably, the technology can help identify a vehicle even when its license plate is not visible, shifting the investigative starting point from a known plate number or suspect to a pattern of electronic signals.
Leonardo emphasizes that while SignalTrace can correlate electronic signals with license plates, it does not decrypt communications, access message content, or collect stored information from devices. The company asserts that the system does not identify individuals or link an electronic signature to a specific person. Instead, any identification would rely on separate investigative steps and external records, such as vehicle registration data.
Leonardo clarified, “SignalTrace data is an investigative lead, not proof of identity.” The company further stated that the data should not be viewed as definitive proof of an individual’s identity or involvement in any incident. Any association with a person requires additional investigative work and corroborating information, adhering to legal requirements and agency protocols.
This raises intriguing privacy questions. Federal privacy guidelines do not limit identifying information to obvious markers like names or Social Security numbers. The National Institute of Standards and Technology indicates that information can be considered personally identifiable if it can distinguish or trace someone’s identity, either independently or when combined with other linkable data. Movement patterns, in particular, can be revealing.
A notable study published in *Scientific Reports* analyzed 15 months of mobility data from 1.5 million individuals, revealing that just four time-and-place points could uniquely identify 95% of the mobility traces in the dataset. Although this study did not examine SignalTrace specifically, it highlights the potential for repeated location patterns to become distinctive, even in the absence of a person’s name.
Moreover, the association between devices can also be significant. For instance, your phone may frequently travel alongside your spouse’s smartwatch, your coworker’s phone, or your child’s tablet during family trips. Such patterns can reveal relationships and connections that may not be immediately apparent.
Research published in the *Proceedings of the National Academy of Sciences* tracked 94 participants using phones that recorded Bluetooth proximity and communication patterns. The findings indicated that this behavioral data could accurately classify 95% of reported friendships among participants. While SignalTrace employs a different methodology, the broader lesson remains: repeated proximity can expose social relationships, which may lead to erroneous conclusions.
For example, two individuals might commute together without any other connection, or a borrowed car could lead to misleading associations. A phone left in a vehicle after its owner departs could also contribute to confusion. While patterns can generate investigative leads, they do not clarify the nature of the relationships involved.
Leonardo acknowledges that SignalTrace is still in its early adoption phase, with a limited number of deployments. The company did not disclose specific numbers regarding agencies or locations utilizing the technology but noted growing interest beyond law enforcement, including applications in parking management and traffic monitoring.
The legal landscape surrounding this technology is evolving. In 2018, the Supreme Court ruled in *Carpenter v. United States* that individuals have a reasonable expectation of privacy concerning historical cellphone location records held by wireless carriers. A more recent ruling in June 2026, *Chatrie v. United States*, involved location data obtained through a geofence warrant. In this case, police investigating a bank robbery accessed Google location data associated with devices near the crime scene, ultimately leading to the identification of a suspect.
The Supreme Court determined that obtaining Chatrie’s cellphone location data constituted a Fourth Amendment search, affirming the reasonable expectation of privacy in cellphone location information. However, the court did not rule on whether the specific geofence warrant met the Fourth Amendment’s probable cause and particularity requirements, leaving that question for lower courts to address.
This distinction is crucial for SignalTrace. While *Chatrie* involved historical location data stored by Google, SignalTrace detects signals broadcast from nearby devices. The Supreme Court has yet to determine whether the collection of data by SignalTrace constitutes a Fourth Amendment search. Nonetheless, both cases contribute to the ongoing conversation about privacy in the context of law enforcement technology.
Consider the implications of SignalTrace in public spaces, such as protests or large gatherings. A device could repeatedly appear near individuals under police scrutiny, but this proximity does not imply any wrongdoing or knowledge of those individuals. The patterns generated could serve as investigative leads, but they cannot explain the context of the associations.
While automated license plate readers have expanded beyond traditional law enforcement applications, the addition of electronic signal analysis could significantly enhance the capabilities of these systems. Although there is no definitive way to prevent roadside sensors from detecting your device, individuals can take steps to reduce wireless activity and limit location tracking on their phones.
Turning off Bluetooth while driving can help minimize exposure, although it may disconnect devices like wireless headphones or smartwatches. Limiting location access may not prevent SignalTrace from detecting a wireless signal, but it can reduce the amount of location information collected by apps.
It is advisable to inquire whether your local police department uses automatic license plate readers and to review their policies regarding data handling, retention periods, and sharing with other agencies. While these measures can mitigate some digital exposure, they cannot render your phone invisible to all roadside sensors.
As SignalTrace technology continues to develop, the conversation around digital privacy becomes increasingly complex. A device identifier does not need to contain a person’s name to be useful in an investigation. While Leonardo asserts that SignalTrace does not identify individuals, the potential for investigators to combine electronic signatures with external information raises important questions about privacy and the implications of such technology.
Ultimately, the challenge lies in balancing the benefits of technology for public safety with the need for robust privacy protections. As society navigates this evolving landscape, it is crucial to consider how much inference we are willing to allow technology to make before stronger legal safeguards are implemented. Would you feel comfortable with law enforcement using electronic signatures from your devices to develop leads about your movements and associations, or should such tracking require a warrant? Let us know your thoughts.
According to CyberGuy, the implications of SignalTrace technology are significant and warrant careful consideration.