SignalTrace, a new technology developed by Leonardo, links wireless device signals to license plate data, raising significant privacy concerns regarding digital tracking by law enforcement.
SignalTrace, a technology developed by security company Leonardo, is designed to detect wireless signals emitted by personal devices and link them to 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, the system records your license plate number along with the time and location. Simultaneously, another sensor picks up wireless signals from devices traveling in proximity, which could include your smartphone, smartwatch, or even Bluetooth headphones. Over time, the software can identify patterns, recognizing that certain signals frequently travel together. This creates a recurring electronic pattern associated with your vehicle.
Weeks later, if one of those signals is detected near a different vehicle involved in an investigation, police may not know who owns the device. However, the established connections could provide investigators with a starting point.
The implications of SignalTrace extend beyond mere tracking; they raise significant privacy concerns. How anonymous is a device signal once it can be linked to a vehicle, repeated locations, and other devices traveling alongside it?
SignalTrace operates in conjunction with automatic license plate reader technology, commonly referred to as ALPR. Traditional license plate readers capture images of passing vehicles, recording their plate numbers, vehicle details, and timestamps. SignalTrace adds another layer by detecting electronic signals from consumer devices, including smartphones, wearables, and RFID devices. The software identifies signals that frequently appear together, allowing it to create what Leonardo describes as an electronic fingerprint for that group.
According to the company’s product information, SignalTrace can connect recurring device patterns with license plate information and time-stamped locations, enabling investigators to search these patterns later. The technology can even assist in recognizing a vehicle when its license plate is not visible, altering the starting point for investigations. Traditionally, police might begin with a plate number or a known suspect, but with SignalTrace, they could potentially start with a recurring pattern of electronic signals and work backward.
Leonardo emphasizes that while SignalTrace can correlate electronic signals with license plates, it does not decrypt communications, access message content, or collect information stored on devices. The company asserts that SignalTrace does not identify individuals or associate electronic signatures with specific persons. However, investigators can correlate an electronic signature with a license plate, and, subject to applicable laws, use vehicle registration records to obtain information about the registered owner. This means that identification would come from separate investigative steps and external records, not from SignalTrace itself.
Leonardo clarifies that “SignalTrace data is an investigative lead, not proof of identity.” The company stresses that any association with a person requires additional investigative work and corroborating information in accordance with legal requirements and agency procedures. They believe that technology should support, not replace, sound investigative practices.
The privacy debate surrounding SignalTrace becomes more complex when considering how identifying information is defined. The National Institute of Standards and Technology notes that information can be considered personally identifiable if it can distinguish or trace someone’s identity, either on its own or when combined with other linkable information. Movement patterns, for instance, can be revealing.
A study published in *Scientific Reports* analyzed 15 months of mobility data from 1.5 million people, finding that just four time-and-place points were sufficient to uniquely characterize 95% of the mobility traces in the dataset. While this study did not examine SignalTrace specifically, it highlights how repeated location patterns can become distinctive, even when a person’s name is absent from the data.
Location is just one aspect of the concern; associations matter as well. Your phone may frequently travel alongside certain other devices, such as your spouse’s smartwatch, a coworker’s phone, or your child’s tablet during family trips. These patterns can reveal relationships, which can lead to misunderstandings. For example, two individuals may commute together without any other connection, or a phone might remain in a vehicle after its owner has left. Such patterns can create investigative leads but cannot explain the nature of the relationship between two people.
Leonardo acknowledges that SignalTrace is still in the early stages of adoption, with a limited number of deployments. The company did not disclose the exact number of agencies or locations currently utilizing the technology but noted that interest is growing beyond law enforcement, with potential applications in parking management, traffic monitoring, and commercial automotive sectors.
The legal questions surrounding SignalTrace arise at a particularly relevant time. In 2018, the Supreme Court ruled in *Carpenter v. United States* that individuals have a reasonable expectation of privacy in historical cellphone location records held by wireless carriers. More recently, in June 2026, the Court decided *Chatrie v. United States*, which involved location information obtained through a geofence warrant. In this case, police investigating a bank robbery used Google location data associated with devices near the crime scene to narrow down their search, ultimately leading to a suspect.
The Supreme Court held that police conducted a Fourth Amendment search when they obtained Chatrie’s cellphone location data from Google, affirming that individuals can have a reasonable expectation of privacy in cellphone location information. However, the Court did not determine whether the specific geofence warrant met the Fourth Amendment’s probable cause and particularity requirements, sending that issue back to a lower court.
This distinction is crucial for SignalTrace. While *Chatrie* involved historical location information stored by Google, SignalTrace detects signals broadcast from nearby devices. The Supreme Court has yet to rule on whether the collection of data by SignalTrace constitutes a Fourth Amendment search, but the cases contribute to the broader conversation about privacy.
Consider a protest, political event, or large public gathering where SignalTrace sensors are operational. A device could repeatedly appear near devices associated with individuals under police scrutiny. However, this proximity does not imply that the device owner is connected to those individuals or engaged in any wrongdoing. It merely provides an investigative lead.
Software can recognize recurring signals and proximity but cannot determine the context of those relationships. This distinction is vital, as Leonardo emphasizes that SignalTrace data is an investigative lead, not proof of an individual’s identity or involvement in an incident. Any conclusions drawn about a person would necessitate further investigative work and corroboration.
As automated license plate readers expand beyond traditional police installations, the integration of electronic signal analysis could enhance the ability to produce a comprehensive picture of movement. While there is no setting on your phone that guarantees invisibility to SignalTrace or similar roadside sensors, you can take steps to reduce wireless activity and limit location tracking.
For instance, turning off Bluetooth while driving can help, although it will disconnect devices like wireless headphones and 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.
To mitigate your digital exposure, consider checking whether your local police department employs automatic license plate readers and whether they publish policies regarding data handling. Understanding retention periods, access, and sharing with other agencies is essential.
While it is important to remain aware of the implications of technologies like SignalTrace, Leonardo maintains that the system is still in its early stages. The company asserts that while it does not identify individuals, the potential for investigators to combine electronic signatures with external information raises significant privacy concerns.
As the definition of identification evolves, it is crucial to consider how much inference we are willing to allow technology to make before stronger legal protections are implemented. Would you be 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 at CyberGuy.com.
According to CyberGuy, the implications of SignalTrace technology are significant and warrant careful consideration regarding privacy rights and law enforcement practices.