Introduction
PrivateID is built for seamless, interoperable identity authentication with complete privacy at its core. Its edge-based architecture and patented tokenization ensure biometric data never leaves the device, enabling scalable, compliant, and frictionless authentication across industries.
ROC, while recognized for accuracy in facial recognition, is primarily designed for surveillance and security-focused workflows. Its reliance on traditional biometric processing introduces scalability challenges, privacy risks, and limited interoperability, making it less suited for modern enterprise and consumer identity needs.
1. Architecture
PrivateID: Performs 1:1 biometric matching directly at the device edge. Biometric data and PII remain securely on the device, preserving privacy while eliminating data breach risks and simplifying global compliance.
ROC: Operates primarily through server-based or on-premise matching, requiring biometric data to be transmitted and stored for processing.
2. Privacy & Tokenization
PrivateID: Uses patented Homomorphic Tokenization, transforming biometrics into irreversible, anonymized tokens. Tokens are unique, cannot be reverse-engineered, and are IEEE 2410 compliant and therefore do not incur global biometric privacy law obligations under GDPR, CCPA, BIPA and HIPAA. There is no risk to data breaches as no biometric data or templates are transmitted.
ROC: Biometric data and templates are transmitted and stored for processing creating linkable templates subject to data breach risks and regulatory exposure.
3. 1:1 vs 1:N Matching
PrivateID: Uses patented homomorphic tokenization to transform biometrics into irreversible, guaranteed unique, and fully anonymized tokens for 1:N biometric processing while performing 1:1 biometric processing at the edge.
•1:1: Edge-based processing, no network transmission. No images or biometric data leave the device.
•1:N Identification: Only anonymized tokens — not biometric images or templates — are transmitted to the server for processing. This delivers scalable, privacy-preserving 1:N search that operates far more efficiently than traditional biometric recognition systems. Processing requires minimal bandwidth (1KB tokens vs multi-megabyte images) and achieves constant-time lookups (~5ms) regardless of gallery size.
ROC: Performs traditional biometric searches with biometric templates/images. Performance depends on infrastructure scale, and efficiency and accuracy decreases with large gallery sizes. Processing sensitive biometric data heightens security and privacy risks.
4. Multi-Modal Biometrics
PrivateID: Supports facial, voice, image of palm, and fingerprint biometrics that can be combined with Passkeys and additional identity signals — such as geolocation, Wi-Fi sniffing, and device fingerprinting — to deliver seamless and secure risk-based authentication (RBA) from any camera- or microphone-enabled device.
ROC: Primarily focused on facial recognition, gun detection and other image detection in video surveillance streams.
5. Liveness Detection (PAD)
PrivateID: On-device, advanced anti-spoofing against photos, masks, and deepfakes without transmitting data. Preserving user privacy while mitigating data breach and global privacy compliance risks.
ROC: Provides liveness detection features but requires data transmission for processing, adding data breach and regulatory compliance risks.
6. Scalability & Efficiency
PrivateID: Unlimited scalability with consistent performance. 5MB image is reduced to ~1KB token, minimizing processing demands. Delivers constant performance — with no loss in speed or accuracy — even as gallery size scales indefinitely.
ROC: Scaling requires proportionally more compute and storage; performance is tied to infrastructure investments. Increased performance degradation as gallery scales, accuracy and speed.
7. Accuracy
PrivateID: Delivers 99.999% accuracy across unlimited gallery sizes. (NIST FRVT leader)
ROC: Known for competitive accuracy in facial recognition (NIST FRVT leaderboards) but still processes traditional templates/images. Recognition accuracy diminishes at scale as gallery size increases.
8. Compliance & Security
PrivateID: Processing is performed at the edge (1:1) or through patented Homomorphic Tokenization (1:N) and does not incur global privacy obligations under GDPR, CCPA, HIPAA, and BIPA. (Annually certified to the IEEE 2410 Standard for Biometric Processing.) No biometric data is stored or transmitted.
ROC: Customers bear responsibility for compliance. Biometric data handling creates ongoing regulatory exposure, data breach risk and lack of user privacy.
9. Deployment & Integration
PrivateID: Lightweight SDK/API for rapid integration across IAM, healthcare, retail, and finance. Software only, no heavy infrastructure required. Runs on general purpose hardware including desktop, mobile, POS terminals and more.
ROC: Deployment often requires on-premise or cloud infrastructure scaled to handle biometric databases. Increasing integration time and processing requirements.
10. Ethics & Trust
PrivateID: Purpose-built for user-consented, privacy-preserving identity verification.
ROC: Strong presence in law enforcement and surveillance, raising civil liberties and ethical concerns.
11. Cost & Total Cost of Ownership (TCO)
PrivateID: Edge processing and tokenization reduce bandwidth, compute, and storage costs by orders of magnitude. Minimal infrastructure required leads to lower long-term operational costs.
ROC: Requires significant investment in compute and storage infrastructure. Costs increase proportionally with gallery size and system scaling.
12. Latency & User Experience
PrivateID: Consistent ~100ms processing ensures real-time authentication without user delays, even at massive scale. Optimized for seamless user experiences.
ROC: Latency grows with gallery size and infrastructure load, which can negatively affect user experience in high-scale deployments.
13. Deployment Flexibility
PrivateID: Operates fully at the edge for 1:1 and hybrid edge-to-server for 1:N with tokenization. No vendor lock-in; works across any environment (cloud, on-prem, hybrid).
ROC: Primarily designed for on-premise or server/cloud processing; limited edge-first flexibility.
14. Ecosystem & Interoperability
PrivateID: Easily integrates with IAM, MFA, Passkeys, and risk-based authentication systems. Standards-based (IEEE 2410, FIDO2) interoperability supports enterprise and consumer use cases.
ROC: Limited interoperability; more focused on surveillance/video analytics use cases than enterprise IAM ecosystems.
15. Bias & Fairness
PrivateID: Tokenization eliminates demographic markers from biometric data, reducing the risk of bias and improving fairness across populations.
ROC: Template-based recognition may reflect inherent dataset biases observed in industry-wide testing (e.g., NIST FRVT).
16. Business & Market Positioning
PrivateID: Positioned for enterprises, healthcare, finance, and retail requiring privacy-first, compliant, and scalable biometric solutions.
ROC: Positioned heavily in law enforcement, surveillance, and defense, raising concerns about ethical use and limiting broader enterprise adoption.
Summary
PrivateID provides a privacy-first biometric platform that performs 1:1 matching at the device edge and leverages patented homomorphic tokenization for 1:N searches. This design ensures biometric data never leaves the device, delivers constant-time performance, unlimited scalability, and multi-modal authentication, while inherently meeting global compliance standards and reducing infrastructure costs.
ROC, by contrast, relies on traditional server-based recognition that transmits and stores biometric templates. While competitive in accuracy, ROC’s performance degrades as gallery sizes grow, increases compliance burdens, and is primarily focused on surveillance and law enforcement, raising ethical and privacy concerns.