Executive Summary: As biometric authentication systems proliferate, the adoption of homomorphic encryption (HE) to secure fingerprint data has been heralded as a breakthrough for privacy. However, new vulnerabilities exposed in 2025–2026—particularly in high-profile breaches such as the SK Telecom incident—underscore critical weaknesses in HE-based biometric systems. This report examines the state of privacy-preserving fingerprint recognition in 2026, identifies attack vectors introduced by HE implementations, and offers actionable recommendations for securing biometric infrastructures against adversarial exploitation.
Homomorphic encryption allows operations (e.g., comparison, matching) to be performed directly on encrypted data without decryption. In biometric systems, this enables secure enrollment, storage, and authentication while preserving user privacy. Systems such as FHE (Fully Homomorphic Encryption)-enabled fingerprint matchers promise compliance with privacy regulations (e.g., GDPR, CCPA) by ensuring that raw biometric data never appears in plaintext during processing.
However, real-world deployments often rely on partially homomorphic schemes (e.g., Paillier, BFV) due to computational overhead, which limits functionality and introduces novel attack surfaces. The reliance on third-party cloud services for HE computations further expands the threat model.
The May 2025 breach at SK Telecom exposed International Mobile Subscriber Identity (IMSI), International Mobile Equipment Identity (IMEI), and authentication keys—core components of SIM-based biometric authentication systems. While the breach did not directly target fingerprint databases, it revealed how biometric authentication chains can be compromised when integrated with legacy authentication infrastructures.
Critically, SK Telecom’s systems reportedly supported biometric login via facial recognition and fingerprint for USIM provisioning. The theft of cryptographic keys allowed attackers to forge authentication tokens, enabling SIM cloning and man-in-the-middle (MITM) attacks. This incident underscores that weak cryptographic hygiene in biometric ecosystems can undermine even the most advanced privacy-preserving techniques.
Homomorphic computations—especially those involving polynomial arithmetic and noise management—are computationally intensive and exhibit timing and power variations. Attackers with local access (e.g., via compromised OS or hypervisor) can profile the HE execution flow to infer biometric features. For instance, differences in matching scores or loop iterations during encrypted comparison can leak information about minutiae points in a fingerprint.
Mitigation: Constant-time HE implementations, hardware-isolated enclaves (e.g., Intel SGX, AMD SEV), and randomized execution paths are essential to neutralize timing attacks.
Many modern biometric systems integrate with OAuth 2.0 or OpenID Connect for identity federation. If HE is applied only to the biometric template but not to the OAuth token exchange, attackers can exploit token leakage to bypass biometric verification entirely.
For example:
Mitigation: Apply HE or confidential computing to the entire authentication pipeline, including token issuance and refresh flows. Use Proof Key for Code Exchange (PKCE) and short-lived tokens to limit replay risk.
HE systems require robust key management for encryption parameters (e.g., modulus, secret keys). The SK Telecom breach demonstrated how poor key hygiene—such as hardcoded or weakly stored cryptographic keys—can lead to catastrophic failure. In HE-based biometrics, a compromised secret key (used for encryption or parameter generation) can allow decryption of enrolled templates or manipulation of matching outcomes.
Mitigation: Use hardware security modules (HSMs) or cloud-based key management services (KMS) with strict access controls and audit logging. Rotate HE keys periodically and bind them to device-specific attestation certificates.
While HE secures templates during storage and matching, it does not inherently prevent presentation attacks (e.g., silicone fingerprints, high-resolution prints). If enrollment is compromised, the HE-protected system will authenticate forged biometrics as genuine—rendering encryption moot.
Mitigation: Combine HE with liveness detection, multi-modal biometrics, and continuous authentication. Enforce strict enrollment procedures with supervised identity proofing.
Deploy HE computations within trusted execution environments (TEEs) such as Intel TDX, AMD SEV-SNP, or ARM TrustZone. This isolates HE operations from the host OS and hypervisor, mitigating side-channel risks and memory inspection attacks.
Apply zero-trust architecture to biometric systems:
Treat HE keys as critical identity infrastructure. Governance must include:
With NIST’s PQC standardization finalized by 2024, HE schemes should be designed to interoperate with PQC algorithms (e.g., CRYSTALS-Kyber for key exchange). Future-proofing biometric systems ensures resilience against quantum decryption threats.
Emerging threats include:
Research priorities include:
Homomorphic encryption remains a cornerstone of privacy-preserving biometrics, but its effectiveness hinges on rigorous implementation, holistic threat modeling, and integration with modern identity governance. The 2025 SK Telecom breach serves as a cautionary tale: biometric systems are only as secure as their weakest link—be it cryptographic keys, token management, or hardware