Biometrics as Entropy Sources

TL;DR

Yes, biometric devices can be used as user-based entropy sources for cryptography and security applications, but it’s complex. Raw data is rarely suitable directly. You need careful pre-processing, noise amplification, and statistical testing to ensure sufficient randomness. Don’t roll your own crypto – use well-vetted libraries.

1. What is Entropy?

In security terms, entropy represents the unpredictability of data. High entropy means it’s difficult to guess; low entropy means it’s easy. Cryptographic systems need a good source of entropy (randomness) for key generation and other operations.

2. Why Biometrics?

Biometric data – fingerprints, iris scans, facial features, keystroke dynamics – is inherently unpredictable. Each person’s biometric characteristics are unique, and even the same person’s measurements vary slightly over time due to factors like skin hydration, lighting conditions, or mood.

3. Challenges with Using Biometrics Directly

1. Low Entropy in Raw Data: A single fingerprint scan isn’t truly random. There are patterns and correlations within the data itself.
2. Sensor Noise: Sensors aren’t perfect; they introduce noise, but this noise might not be sufficiently random.
3. Presentation Attacks (Spoofing): Biometric systems can be fooled with fake fingerprints or images. This affects the security of entropy derived from compromised sensors.
4. Environmental Factors: Lighting, temperature, and other conditions affect biometric readings. These are not truly random but introduce variability that needs to be accounted for.
5. Data Bias: Biometric data can vary across demographics; a system trained on one group might perform poorly on another.

4. Steps to Use Biometrics as an Entropy Source

1. Sensor Selection: Choose a high-quality biometric sensor with good resolution and sensitivity. Consider sensors designed for security applications, not just convenience.
2. Data Acquisition: Collect multiple samples from the user over time. This helps to average out short-term variations and increase entropy.
3. Pre-processing: Clean and normalize the data. This might involve removing outliers, correcting for sensor drift, or aligning images. Avoid operations that introduce bias.
4. Feature Extraction: Extract relevant features from the biometric data. For fingerprints, this could be minutiae points (ridge endings and bifurcations). For facial recognition, it could be distances between key landmarks.
5. Noise Amplification: Enhance the randomness of the extracted features. Techniques include:
   • Hashing: Apply a cryptographic hash function to the data. This spreads out the entropy and makes it more difficult to reverse engineer.

     python
     import hashlib
     
     def hash_data(data):
         return hashlib.sha256(data.encode()).hexdigest()
     

   • Randomization Functions: Combine the biometric data with a source of external randomness (e.g., system clock, hardware random number generator).
6. Statistical Testing: Rigorously test the resulting entropy for randomness using statistical tests like:
   • Dieharder Test Suite: A comprehensive suite of tests for randomness.
   • NIST Statistical Test Suite: Another widely used set of tests.
7. Entropy Pooling: Combine the entropy derived from biometrics with other sources of randomness to create a stronger overall entropy pool.

5. Important Considerations

• Security Architecture: Integrate the biometric entropy source into a secure architecture that protects against attacks.
• Key Derivation Functions (KDFs): Use a KDF to derive cryptographic keys from the entropy pool. This adds an extra layer of security and ensures that the keys are resistant to brute-force attacks.
• Regular Updates: Update the biometric data regularly to prevent attackers from learning patterns over time.
• Don’t Roll Your Own Crypto: Use well-established cryptographic libraries (e.g., OpenSSL, libsodium) that have been thoroughly vetted by security experts.