December 09, 2024
Anonybit Team
Debunking Myths Part 3: Why Biometric Templates Need Protection
Biometric technology has transformed the way we authenticate identity, delivering both convenience and enhanced security. However, like any transformative technology, it has faced scrutiny and generates concern —particularly around the issue of bias, legality, privacy, covert usage and the risks associated with AI. While the concerns are valid, they are often misunderstood or even exaggerated.
This blog is part of a five-part series dedicated to unpacking and debunking common myths surrounding biometrics, offering clarity on where the challenges lie and how the industry has been addressing them.
This is the third installment, focusing on biometric template security.
See the other posts in this series:
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Biometrics, such as fingerprints, facial recognition, and iris scans, are widely heralded as the gold standard for secure authentication. They’re unique to each individual, convenient to use, and increasingly embedded in everything from smartphones to payment systems and border control processes. Yet, alongside their adoption, misconceptions and myths have proliferated—particularly regarding the security of biometric templates.
One persistent myth, perpetuated by many in the industry itself, is that biometric data, once captured and converted to mathematical formats known as vectors or templates, is inherently safe and cannot be exploited. Let’s delve into this misconception, explore why templates are indeed vulnerable, and emphasize the critical need for robust protection mechanisms.
Myth #1: Biometric Templates Cannot Be Reverse Engineered
A common misunderstanding is that biometric templates—mathematical representations of biometric traits—are irreversible. This belief stems from the notion that converting a face or fingerprint into a template loses enough information to make reconstruction impossible. However, research and real-world examples prove otherwise.
The fact is, attackers with access to poorly protected templates can use advanced techniques like inverse transformation algorithms that leverage mathematical processes that created the original template, to reverse-engineer it back into a form resembling the original biometric data.
As an example of this, in 2017, researchers from New York University and Michigan State University demonstrated how partial fingerprint templates could be reverse-engineered into “MasterPrints” capable of spoofing biometric systems. By exploiting the mathematical algorithms used to create templates, they generated synthetic fingerprints that matched multiple user templates stored in poorly protected systems. There have been other research papers demonstrating the same concept.
Myth #2: It’s Okay to Be Lax About Biometric Protection Because Images Are Public
Another myth is that securing biometric templates isn’t critical because biometric traits like faces or voices are already “public” (e.g., in photos or videos available online). While it’s true that photos and voice recordings may be available, the templates derived from them are vastly different in their purpose and usage in that they are designed to be used with specific algorithms.
In addition, if implemented properly with liveness detection systems, biometric systems should be able to recognize a photo, video or voice replays. Liveness detection systems work by analyzing dynamic traits such as movement, texture, or responses to stimuli (e.g., blinking, head movements, or voice modulations) to ensure that the biometric input is coming from a live person rather than a static image, pre-recorded video, or synthesized voice.
The real danger then is not about the images being public, it is using stolen templates in an injection attack to bypass authentication systems. Injection attacks involve feeding stolen or synthetic biometric data directly into a system at a point where it expects a live biometric input, such as a sensor or camera. Since many systems rely solely on template matching for verification, they may accept this injected data as legitimate, allowing attackers to impersonate the rightful user. When attackers gain access to biometric templates—whether through data breaches, intercepted transmissions, or improperly secured storage—they can exploit these templates to compromise authentication processes.
Injection attacks highlight the critical need for privacy-by-design systems that combine strong encryption, template distribution, and liveness detection to ensure that only genuine, live biometrics are accepted during authentication.
Myth #3: Standard Encryption Is Enough To Protect Biometric Templates in Centralized Databases
Many assume that encrypting biometric templates is sufficient to keep them safe. While encryption is a critical tool for securing sensitive data, it is not infallible. Biometric systems face several challenges that standard encryption alone cannot address, including risks from decryption processes, system vulnerabilities, insider threats, and the looming impact of quantum computing.
With traditional encryption, the biometric templates must be decrypted during the authentication process to compare the stored template with the presented biometric data. This step introduces a critical window of vulnerability, as the decrypted template could be intercepted or accessed if the system is compromised.
Similarly, if the encryption keys are compromised, the encrypted biometric templates become as vulnerable as unencrypted data. Keys may be stolen during a system breach, obtained through social engineering attacks, or leaked by malicious insiders. Without the keys, the strength of encryption becomes irrelevant, leaving templates exposed to misuse or reverse engineering as noted above. Moreover, since decrypted templates are often processed on a single server, a successful breach of the encrypted templates can compromise the security of an entire system.
To further emphasize the point, quantum computing introduces a whole new level of risk, as it could break standard encryption schemes in a fraction of the time required by classic attacks. Attackers can decide to gain access to encrypted templates today and apply quantum computing to decrypt them later.
The Best Ways to Protect Biometric Templates
The myths surrounding biometric templates often downplay the need for their protection. However, understanding the real risks helps guide organizations towards prioritizing robust privacy by design frameworks while benefiting from the security and user experience benefits that biometrics provide.
The best techniques for protecting biometric templates involve getting rid of them in the first place. The latest innovations leverage multi-party computation and zero-knowledge proofs to eliminate centralized repositories and the need to decrypt or reconstruct biometric data for matching. In these models, no single entity has access to the full biometric template at any point, making it much harder for attackers or insiders to compromise the system.
Protecting the Future of Biometric Security
Biometric templates are powerful tools, and they must be treated as such. Dismissing the risks of template reverse engineering or oversimplifying protection measures exposes organizations and individuals to significant threats. Dispelling these myths is a crucial step in fostering greater awareness of the vulnerabilities and advancing the adoption of robust security practices.
By taking proactive measures to protect biometric templates, we can ensure that these technologies remain secure, trustworthy, and capable of meeting the demands of an increasingly digital world. To learn about how Anonybit protects biometric security, click here.