Encrypted Biometric Data

Traditional biometric authentication systems store raw biometric data—such as fingerprints, facial images, or voice samples—directly in centralized databases, creating significant privacy and security vulnerabilities. A single breach could expose sensitive user information, and the centralized nature of these systems makes them prime targets for attackers. Additionally, such approaches often struggle to comply with stringent privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). InterLink ID revolutionizes this paradigm by employing zero-knowledge proofs (ZKP) and homomorphic encryption to transform biometric features into irreversible, encrypted representations. This enables secure, decentralized identity verification without ever storing or exposing raw biometric data, striking a balance between uniqueness (each individual has a distinct encrypted identity) and privacy (no sensitive information is revealed).

Here’s a detailed breakdown of the privacy-preserving biometric encryption process in InterLink ID:

Feature Extraction: The process begins with extracting distinctive features from a biometric input, such as a facial image, fingerprint, or iris scan. InterLink ID uses advanced deep learning models—such as ResNet or Vision Transformers (ViT)—pre-trained on large datasets to map the raw biometric data $B$ into a high-dimensional feature vector $F$:

$$F=f(B) \in \mathbb{R}^d$$

where $d$ represents the dimensionality of the feature space, and $f(\cdot)$ is the deep learning-based feature extractor. These models excel at capturing robust, unique characteristics, making them ideal for reliable identity verification.

Quantization and Secure Transformation: To enhance privacy and security, the feature vector $F$ undergoes a series of transformations:

• Biometric Salting and Random Projection Hashing: A randomized orthogonal transformation T T T is applied to decorrelate the feature components, preventing attackers from reconstructing the original features:

$$F'=TF$$

where $T$ is a randomly generated orthogonal matrix.

Locality-Sensitive Hashing (LSH): The transformed vector $F'$ is then mapped to a fixed-length binary hash using an LSH function:

$$H(F')=(h_1, h_2, ..., h_m), \hspace{6pt} h_i=\text{sign}(w_i^TF'+b_i)$$

where $w_i$ are random projection vectors, and $b_i$ are bias terms. LSH ensures that similar biometric inputs produce similar hashes, enabling approximate matching while obscuring exact feature values.

Zero-knowledge Biometric Encryption: Rather than storing the biometric hash $H(F')$ directly, InterLink ID employs a zero-knowledge proof (ZKP)-based commitment scheme using a Pedersen commitment:

$$C=g^{H(F')}h^r \hspace{6pt} \text{mod } p$$

where $g$ and $h$ are generators of a cyclic group, $r$ is a random blinding factor, andis a large prime number. This commitment allows the system to store a representation of the biometric hash without revealing it, ensuring that even if the stored data is accessed, the original biometric information remains protected.

Decentralized Verification: The commitment $C$ is stored in the Decentralized InterLink ZK Biometric Node Pool, a distributed ledger-based trust pool. During authentication, the user (prover) generates a zero-knowledge proof of knowledge (ZKPoK) to demonstrate that their biometric hash matches the stored commitment $C$ without disclosing $H(F')$. The decentralized node pool collectively verifies this proof, ensuring a secure and privacy-preserving authentication process with no centralized point of failure.

AI-Powered Encryption Enhancements: InterLink ID integrates state-of-the-art AI techniques to bolster the encryption process:

• Self-Supervised Learning (SSL): Models like SimCLR and BYOL extract robust feature representations without labeled data, reducing overfitting and enhancing resilience against adversarial attacks.

• Differential Privacy (DP): Noise is injected into the biometric features before hashing, preventing reconstruction of the original input even if an attacker accesses the hashed data.

• Generative Adversarial Networks (GANs): Synthetic biometric data generated by GANs is used to test the system’s robustness against spoofing, ensuring it can distinguish genuine inputs from fabricated ones.

Security and Privacy Guarantees: This approach delivers several key benefits:

• Irreversibility: The multi-step transformation and encryption process makes it computationally infeasible to recover the original biometric data.

• Cancelability: If a biometric encryption is compromised, a new transformation matrix $T$ can be issued, allowing users to re-enroll without altering their underlying biometrics.

• Decentralization: Distributed storage and verification eliminate single points of failure, enhancing system resilience.

Compliance with Privacy Regulation: InterLink ID’s design inherently aligns with global privacy regulations, such as GDPR and CCPA. By never storing raw biometric data and using zero-knowledge proofs for verification, the system minimizes the risk of unauthorized data exposure, a key requirement under GDPR’s data minimization principle. The decentralized architecture ensures transparency and accountability, allowing users to maintain control over their personal information as mandated by CCPA. This compliance not only mitigates legal risks but also builds trust with users and stakeholders in privacy-sensitive markets.

Real-World Applications: InterLink ID’s privacy-preserving biometric encryption has broad applicability across industries. In finance, it enables secure customer onboarding and transaction authentication without exposing biometric data, reducing fraud risks. In healthcare, it protects patient identities during telemedicine or electronic health record access, ensuring compliance with HIPAA alongside GDPR. For government services, it facilitates secure voter verification or border control, balancing security with citizen privacy. These applications highlight the versatility and real-world impact of InterLink ID’s technology.

Innovation in Biometric Security: Unlike traditional biometric systems that rely on centralized storage and direct template matching, InterLink ID integrates zero-knowledge proofs with AI-driven feature extraction to achieve unparalleled privacy and security. Conventional methods expose raw data during breaches and lack cancelability, whereas InterLink ID’s encrypted, decentralized approach ensures data remains protected and renewable. This fusion of cryptography and artificial intelligence sets a new benchmark for biometric authentication, addressing longstanding privacy challenges in digital identity management.

Future-Proofing the System: InterLink ID is engineered to evolve with emerging threats and technological advancements. The modular use of AI models allows for seamless integration of next-generation feature extractors, such as improved Vision Transformers or quantum-resistant neural networks. Similarly, the cryptographic framework supports updates to zero-knowledge protocols as quantum computing advances, ensuring long-term security. This adaptability positions InterLink ID as a resilient solution for the future of digital identity verification.

Figure 1: Privacy-Preserving Biometric Encryption in InterLink ID.

The privacy-preserving biometric encryption technique in InterLink ID presents a groundbreaking approach to secure identity verification. By combining it with federated learning, InterLink ID strikes a balance between uniqueness (each human has one encrypted representation) and privacy (no raw biometric data exposed).