Innovative Tech WhoFi, Scanary Test Biometric Privacy Limits

Published Date : 7/29/2025 

Discover how two groundbreaking technologies, WhoFi and Scanary, are revolutionizing surveillance and security while raising new ethical and privacy concerns. 

Mass surveillance and crowd security are areas where biometrics often collide with privacy and social repression concerns. However, what if you could determine what a person has in their bag from a distance without using biometrics? What if public biometric systems could track individuals without capturing their facial images or matching them to an identity? Both scenarios are now possible with emerging technologies.

The radiofrequency electromagnetic fields that carry Wi-Fi signals can be utilized to gather biometric data, while an Israeli startup has developed a method to use electromagnetic waves for radar screening to detect weapons and hidden objects without relying on biometric data.

WhoFi: Biometric Signatures from Wi-Fi Signals

Researchers have unveiled a biometric system capable of identifying individuals based solely on how they disturb Wi-Fi signals. Unlike traditional fingerprint scanners or facial recognition sensors, the new technology, dubbed “WhoFi,” does not require visual information or direct physical contact. As long as a Wi-Fi network is present, WhoFi can identify people over a wider area than stationary cameras.

WhoFi was developed by Danilo Avalo, Daniele Pannone, Dario Montagnini, and Emad Emam at the Department of Computer Science at La Sapienza University of Rome. Their system relies on the waveform moving through an environment, which changes as it interacts with the physical characteristics of objects and people along its path. These alterations are captured in the form of Channel State Information (CSI) and contain detailed biometrics.

“Unlike optical systems that perceive only the outer surface of a person,” the paper published on arxiv describes, “Wi-Fi signals interact with internal structures, such as bones, organs, and body composition, resulting in person-specific signal distortions that act as a unique signature.”

By training a deep neural network to read these patterns, the researchers achieved identification accuracy rates of 95.5 percent, even in varied settings. This isn’t their first experiment with Wi-Fi-based identification. In 2020, they introduced “EyeFi,” a similar concept, though WhoFi reportedly delivers more precise results.

While the technology offers clear surveillance advantages, it also raises ethical concerns. The possibility of tracking individuals without their consent or awareness is a significant privacy and ethics issue. The team emphasizes that their re-identification system doesn’t collect personal data or establish a person’s identity, instead relying solely on the biometric patterns embedded in Wi-Fi CSI.

As a research project, WhoFi has no immediate commercial or governmental aim. However, its potential in surveillance is evident. “By leveraging non-visual biometric features embedded in Wi-FI CSI, this study offers a privacy-preserving and robust approach for Wi-FI-based Re-ID, and it lays the foundation for future work in wireless biometric sensing,” the academics wrote.

Scanary: Faster Security Checks Without Biometrics

Israeli startup Scanary has unveiled a patented radar security-screening system capable of processing up to 25,000 people per hour across a 200-square meter area without physical contact or chokepoints. The technology flags hidden objects on individuals in motion, identifying potential threats in under two seconds and instantly notifying security teams.

Under the development of Dr. Gideon Levita, a former senior radar engineer for Israel’s Iron Dome and Trophy systems, Scanary combines radar sensors with AI to scan crowds from as far as 10 meters away. The system has the potential to eliminate the need for pocket emptying and physical pat-downs, speeding up security checks at large-scale events and airports, and complying with CE and GDPR requirements by avoiding face biometrics and preserving privacy.

Scanary uses patented electromagnetic imaging to generate three-dimensional views of concealed items, automatically distinguishing innocuous objects like phones or keys from weapons, including those made from non-metallic materials. Augmented reality overlays then pinpoint exact threat locations for rapid intervention.

Scanary has received regulatory approvals in Europe and is now pursuing clearance in the U.S. as it plans pilots in both Israel and the U.S. in the months ahead. The company was founded in 2024, comprises 10 employees, and has raised $3.5 million in a pre-seed investing round. “By combining artificial intelligence, advanced imaging, and computer vision, we enable threat detection from a distance — without stopping people, creating queues, or compromising privacy,” said CEO Ronen Yeshvitz. 

Frequently Asked Questions (FAQS): 

Q: What is WhoFi?

A: WhoFi is a biometric system that identifies individuals based on how they disturb Wi-Fi signals. It uses Channel State Information (CSI) to capture detailed biometrics without visual information or physical contact.

Q: How accurate is WhoFi?

A: WhoFi has achieved identification accuracy rates of 95.5 percent, even in varied settings, according to the researchers at La Sapienza University of Rome.

Q: What ethical concerns does WhoFi raise?

A: WhoFi raises ethical concerns about tracking individuals without their consent or awareness, despite the system not collecting personal data or establishing a person’s identity.

Q: What is Scanary's radar security-screening system?

A: Scanary's system uses radar sensors and AI to scan crowds from up to 10 meters away, identifying hidden objects and potential threats in under two seconds without physical contact or chokepoints.

Q: How does Scanary comply with privacy regulations?

A: Scanary complies with CE and GDPR requirements by avoiding face biometrics and preserving privacy. It uses patented electromagnetic imaging to generate three-dimensional views of concealed items without collecting personal data. 

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