The Dignity of Detection: Architecting Privacy-Preserving AgeTech with Edge AI and mmWave Radar

Executive Summary

The demographic transformation of the 21st century has precipitated a crisis in elder care that is as much ethical as it is logistical. As the global population ages, the imperative to monitor safety—specifically the detection of falls, which constitute the leading cause of injury-related death among adults over 65—has collided violently with the fundamental human right to privacy. The industry's historical reliance on optical surveillance and wearable telemetry has created a "Panopticon of Care," where safety is purchased at the cost of dignity. This whitepaper, authored by Veriprajna, argues that this trade-off is a failure of engineering imagination, not an inevitability of care.

Veriprajna posits that the future of AgeTech lies in the convergence of Millimeter-Wave (mmWave) Radar and Deep Edge Artificial Intelligence . Unlike "wrapper" solutions that merely pipe sensor data to cloud-based Large Language Models, true innovation requires a vertical integration of signal physics, digital signal processing (DSP), and advanced neural architectures running on resource-constrained edge devices.

This document serves as a comprehensive technical and strategic dossier for enterprise stakeholders, healthcare providers, and technology architects. It details the transition from theoretical AI models to deployed, robust safety solutions. We explore the physics of 60GHz sensing, the intricacies of Deep Learning on the Edge, the suppression of environmental clutter, and the integration of these systems into legacy nurse call infrastructures adhering to UL 1069 standards. We demonstrate that by processing dense point clouds and micro-Doppler signatures locally on the device, we can achieve safety without surveillance, keeping the elderly safe without "watching them naked."

1. The Privacy-Safety Dilemma in an Aging World

1.1 The Demographic Imperative and the Fall Epidemic

The statistics are stark and universally acknowledged, yet they bear repeating to frame the engineering challenge. Falls are not merely accidents; they are systemic failures of the monitoring environment. According to the Centers for Disease Control and Prevention (CDC), falls are the leading cause of injury among adults aged 65 and older. ¹ The economic burden is staggering, with annual healthcare expenditures for non-fatal falls reaching approximately $50 billion in the United States alone. ²

However, the cost extends beyond the balance sheet. The "fear of falling" induces a psychological contraction in the elderly, leading to self-imposed restrictions on mobility, social isolation, and accelerated physical decline. ¹ The goal of AgeTech, therefore, is not merely to detect the impact but to create an environment of pervasive safety that restores confidence. The challenge is that the most dangerous spaces—the bathroom and the bedroom—are also the most private.

1.2 The Failure of Legacy Modalities

The traditional response to this crisis has relied on two primary modalities: optical surveillance (cameras) and wearable telemetry (pendants/watches). Both suffer from critical failure modes that render them insufficient for the next generation of care.

1.2.1 The Optical Panopticon

Computer vision has achieved remarkable accuracy in human activity recognition (HAR). However, its deployment in private spaces is ethically fraught. Optical systems capture Personally Identifiable Information (PII) by default. Even with "privacy masking" or low-resolution streaming, the potential for abuse or data leakage creates a psychological burden on the resident.³

Furthermore, optical systems are physically limited:

●​ Illumination Dependency: Cameras fail in low-light conditions or require intrusive infrared illumination that can disrupt circadian rhythms. ⁵

●​ Occlusion: Cameras cannot "see" through shower curtains, blankets, or privacy screens, leaving critical blind spots. ⁵

●​ Dignity: The mere presence of a lens destroys the sense of solitude essential for mental well-being. ⁶

1.2.2 The Compliance Gap of Wearables

Wearable devices (PERS) solve the visual privacy issue but introduce the "compliance gap." Effectiveness is contingent on the user remembering to wear and charge the device. Cognitive decline, sensory issues, or simple forgetfulness render these devices useless in a significant percentage of fall events. Crucially, many falls occur at night when wearables are removed for sleeping or charging, or during bathing when waterproof capabilities are often distrusted by users. ⁷ A "passive" system—one that requires no user interaction—is the only failsafe approach.

1.3 The Veriprajna Philosophy: Deep AI, Not Just APIs

Veriprajna distinguishes itself in the marketplace by moving beyond superficial "wrapper" solutions. We do not simply stream sensor data to a cloud API. We architect Deep AI solutions that operate at the signal level. By leveraging Frequency Modulated Continuous

Wave (FMCW) radar technology combined with Edge AI, we extract semantic understanding (e.g., "Person A has fallen") from raw electromagnetic reflections without ever reconstructing a visual image. This ensures privacy by physics, not just by policy.

2. Fundamental Physics of Privacy-Preserving Sensing

To engineer a solution that respects privacy while ensuring safety, one must select a sensing modality that is physically incapable of capturing biometric identity (like a face) while remaining highly sensitive to biological motion (like a heartbeat). Millimeter-wave (mmWave) radar is uniquely positioned to meet these contradictory requirements.

2.1 The Electromagnetic Spectrum: Why 60 GHz?

Radar operates by transmitting electromagnetic waves and analyzing their reflections. The choice of frequency determines the interaction with the environment.

●​ 24 GHz (ISM Band): Historically used for simple motion detection (like automatic doors). It suffers from lower bandwidth (limited range resolution) and larger antenna requirements. ⁸

●​ 77 GHz: The standard for automotive radar (adaptive cruise control). While it offers excellent range (300m+), it is optimized for tracking fast-moving vehicles outdoors. ⁵

●​ 60 GHz (V-Band): The optimal choice for indoor human monitoring.

○​ Fine Resolution: At 60 GHz, the wavelength ($\lambda$) is approximately 5mm. This allows for the detection of "micro-motions"—sub-millimeter displacements of the chest wall caused by breathing and heartbeats. ⁵

○​ Privacy Containment: The 60 GHz band sits within the oxygen absorption spectrum, meaning signals attenuate rapidly over distance and do not penetrate thick concrete walls effectively. This prevents the "leakage" of monitoring data outside the intended room, adding a layer of physical data security. ⁸

○​ Regulatory Bandwidth: Regulatory bodies allocate wide bandwidths (up to 4 GHz) in the 60 GHz band for unlicensed use. Range resolution ($d_{res}$) is inversely proportional to bandwidth ($B$): ​ ​ $$d_{res} = \frac{c}{2B}$$ ​ ​ A 4 GHz bandwidth yields a range resolution of roughly 3.75 cm. This granularity allows the system to distinguish a person’s limbs from their torso, a critical factor in differentiating a fall from a crouch.8

2.2 Frequency Modulated Continuous Wave (FMCW) Mechanics

Unlike pulsed radar, which measures time-of-flight directly, or simple continuous-wave Doppler radar, which only measures velocity, Veriprajna utilizes FMCW radar. The transmitter sends a "chirp"—a sinusoid whose frequency increases linearly with time. When this signal reflects off an object and returns to the receiver, the transmitted signal has moved to a higher frequency. The difference between the transmitted frequency ($f_{tx}$) and the received frequency ($f_{rx}$) at any instant is the beat frequency ($f_b$). This beat frequency is mathematically related to the distance ($R$) to the target:

$$f_b = \frac{S 2 R}{c}$$

Where $S$ is the slope of the frequency ramp and $c$ is the speed of light. By performing a Fast Fourier Transform (FFT) on the beat signal, we convert the signal from the time domain to the frequency domain, where peaks correspond to the distance of objects. ¹⁰

2.3 The 4D Sensing Paradigm

Traditional sensors are 1D (distance) or 2D (images). FMCW radar provides a 4D dataset:

1.​ Range ($R$): Distance to the target (derived from beat frequency).

2.​ Velocity ($v$): Speed of the target relative to the sensor (derived from the phase shift across multiple chirps, known as the Doppler effect). ⁵

3.​ Azimuth ($\theta$): Horizontal angle (derived from the phase difference between horizontally spaced antennas).

4.​ Elevation ($\phi$): Vertical angle (derived from the phase difference between vertically spaced antennas).

This 4D data structure allows the AI to perceive the world not as a flat picture, but as a dynamic volume of moving points. Unlike LiDAR, which provides high-resolution geometry but no instantaneous velocity, radar provides Doppler velocity for every point. This is the "secret sauce" of radar AI: we can separate a stationary chair from a breathing human, even if the human is sitting perfectly still, because the human generates micro-Doppler modulations. ⁹

3. Signal Processing Architectures for Robust Detection

The raw data from a radar sensor is not immediately usable by a neural network. It is noisy, complex, and high-bandwidth. Veriprajna’s expertise lies in the "Signal Processing Chain"—the algorithmic pipeline that transforms raw ADC data into semantic features.

3.1 The Radar Data Cube

The fundamental data structure in modern radar processing is the Radar Data Cube . It is constructed through a sequence of mathematical transformations:

1.​ Range FFT (Fast-Time): Performed on the samples within a single chirp. This resolves the range of reflections. ¹³

2.​ Doppler FFT (Slow-Time): Performed across the sequence of chirps in a frame. This resolves the velocity of reflections. ¹⁰

3.​ Angle FFT: Performed across the array of antennas. This resolves the spatial location. ¹⁴ This cube contains the power intensity for every voxel in the $(Range, Velocity, Angle)$ space.

3.2 Clutter Mitigation: The Art of Seeing Through Noise

Indoor environments are hostile to radar. Walls, furniture, and ceilings generate massive reflections (clutter) that can drown out the reflection from a human body.

●​ Static Clutter Removal: The simplest approach is to subtract the mean value of the samples across the Doppler dimension. This effectively removes objects with zero velocity (walls, furniture). However, this naive approach can inadvertently remove a person who has fallen and is lying unconscious (and thus static).

●​ Adaptive Clutter Filtering: Veriprajna implements advanced adaptive filtering. We utilize the phase stability of the signal to distinguish between "dead" static objects (walls) and "living" static objects (unconscious humans). Even a motionless human has chest wall displacement (breathing), which creates a subtle phase modulation that our algorithms preserve while suppressing the wall reflections. ¹⁵

3.3 Constant False Alarm Rate (CFAR) Detection

Once clutter is suppressed, we must detect "targets" (points of interest) against the background noise floor. A fixed threshold is insufficient because the noise floor varies with temperature and interference. We employ CFAR (Constant False Alarm Rate) algorithms, which dynamically adjust the threshold based on the local noise environment.

3.3.1 CFAR Methodologies

●​ CA-CFAR (Cell Averaging): Calculates the threshold based on the average power of neighboring cells. This works well in uniform noise but degrades when multiple targets are close together. ¹⁷

●​ OS-CFAR (Ordered Statistic): This is the industry standard for multi-target environments. It sorts the power values of neighboring cells and selects the $k$-th rank value as the noise estimate. This is robust against "masking," where a strong reflection (e.g., a metal cabinet) hides a weaker reflection (a person) nearby. ¹⁹

●​ Deep CFAR: Emerging research suggests replacing classical CFAR with lightweight neural networks (CNNs) that learn the statistical distribution of clutter and target peaks, offering superior performance in complex environments. ¹⁴ Veriprajna is pioneering the deployment of these "Neural Detectors" on edge hardware.

3.4 Feature Extraction: Micro-Doppler and Point Clouds

The output of the detection stage is two-fold:

1.​ Micro-Doppler Spectrograms: By stacking Doppler FFTs over time, we create a visual representation of velocity. A human walking produces a distinct pattern (torso moving at constant speed, limbs oscillating at higher speeds). A fall produces a sudden, broadband burst of energy (acceleration) followed by zero velocity. ¹²

2.​ Point Clouds: A set of $(x, y, z, v, SNR)$ tuples representing detected targets in 3D space. This allows for trajectory tracking and posture analysis. ²³

4. Deep Learning Paradigms for Radar Perception

While classical signal processing detects motion, Deep Learning is required to understand intent and context . Is the motion a fall, or just a person lying down on a bed? Veriprajna leverages three distinct AI architectures to solve this classification problem.

4.1 Convolutional Neural Networks (CNNs) on Spectrograms

We treat the Micro-Doppler spectrogram as an image and apply computer vision techniques.

●​ Architecture: We utilize Deep Convolutional Neural Networks (DCNNs). Unlike the massive ResNet models used for ImageNet, we design lightweight, shallow CNNs optimized for the specific texture of radar spectrograms. ²⁵

●​ Feature Learning: The CNN learns to recognize the "shape" of a fall. A fall signature typically exhibits a high-energy "torso flash" at low frequencies, accompanied by high-frequency "limb flashes," followed immediately by a cessation of signal. In contrast, "sitting down" shows a gradual deceleration. ²²

●​ Performance: Benchmark studies indicate that CNN-based approaches on spectrograms consistently outperform classical machine learning (SVM, Random Forest) by margins of 7-10% in accuracy, primarily due to their ability to learn invariant features robust to noise. ²⁵

4.2 Point Cloud Processing: PointNet and GNNs

Spectrograms lack spatial context. They know how fast something moved, but not where . To solve this, we process the 3D point cloud.

●​ PointNet: Standard CNNs cannot handle point clouds because they are unordered and sparse. PointNet is a specialized architecture that consumes raw point sets and learns spatial features (e.g., the vertical distribution of points). It can distinguish a person standing (vertical column of points) from a person lying down (horizontal spread of points). ³

●​ Graph Neural Networks (GNNs): We treat the radar points as nodes in a graph, with edges representing spatial proximity. GNNs allow the model to learn the geometric relationship between body parts, effectively "skeletonizing" the reflection without a camera. ³

4.3 Sequence Modeling: LSTM and Transformers

A fall is a temporal event: Standing $\rightarrow$ Unstable $\rightarrow$ Falling $\rightarrow$ Impact $\rightarrow$ Lying. Static frame analysis often fails to capture this causality.

●​ CNN-LSTM: We employ a hybrid architecture where a CNN extracts spatial features from each frame, which are then fed into a Long Short-Term Memory (LSTM) network. The LSTM maintains a "memory" of the sequence, allowing it to differentiate between a stumble (recovery) and a fall (no recovery). ²⁷

●​ Radar Transformers (RadMamba): The cutting edge of research involves Transformer architectures (like Vision Transformers or ViT) adapted for radar. These models use "self-attention" mechanisms to weigh the importance of different time steps and frequency bins. Recent innovations like RadMamba utilize State Space Models (SSMs) to handle long sequences of radar data with linear computational complexity, making them more efficient than Transformers for edge deployment. ²⁹

4.4 Sensor Fusion: The "Dual Stream" Approach

The most robust systems use Sensor Fusion . Veriprajna implements a Dual-Stream Network:

1.​ Stream A (Spectrogram): Analyzes velocity dynamics (Micro-Doppler).

2.​ Stream B (Point Cloud): Analyzes spatial trajectory (Height-Time).

3.​ Fusion Layer: Combines these insights.

●​ The "Sit vs. Fall" Solution: A classic radar problem is the "hard sit." A person collapsing onto a sofa generates a Doppler spike similar to falling. By fusing the streams, the AI sees the high velocity (Stream A) but notes that the final centroid height is $z \approx 0.5m$ (Stream B), correctly classifying it as "Sitting" rather than "Falling" ($z \approx 0m$). ³¹

Table 1: Comparative Analysis of AI Architectures for Radar

Architecture	Input Data	Key Strength	Computation al Cost	Best For
2D CNN	Micro-Doppler Spectrogram	High accuracy in distinguishing motion types (e.g., Walk vs. Fall)	Medium	Classifcation of dynamic events

PointNet / GNN	3D Point Cloud	Spatial context (Posture recognition, Height analysis)	High	Static posture analysis (Lying vs. Sititng)
LSTM / GRU	Sequence of Features	Temporal continuity; distinguishing stumbles from falls	Low-Medium	Time-series tracking
Transformer / ViT	Radar Cube / Patches	Global context awareness; handling long dependencies	Very High	Complex multi-person scenarios
Veriprajna Dual-Stream	Fusion (Spectrogram + Point Cloud)	Combines velocity dynamics with spatial accuracy	Optimized	Robust, enterprise-gra de fall detection

5. Edge Intelligence: Engineering Constraints and Solutions

Sending high-bandwidth radar data to the cloud for processing is a privacy risk, a latency bottleneck, and a bandwidth cost. Veriprajna advocates for Edge AI, where the neural network inference occurs directly on the sensor's embedded processor.

5.1 Hardware Platforms: The Silicon Enablers

We target high-performance microcontrollers (MCUs) and Digital Signal Processors (DSPs) optimized for RF sensing.

●​ Texas Instruments (TI) mmWave: Devices like the IWRL6432 or IWR6843 are "Systems-on-Chip" (SoC). They integrate the RF front-end, a C674x DSP for signal processing (FFTs), and an ARM Cortex-R4/M4 for the application logic. This tight integration allows for extremely low latency. ⁵

●​ Infineon XENSIV: The BGT60TR13C pairs with external high-performance MCUs (like STM32 or PSoC). The trend toward "Antenna-in-Package" (AiP) reduces the physical form factor, making sensors unobtrusive and easy to install in consumer environments. ³⁴

5.2 Optimizing for the Edge: TensorFlow Lite for Microcontrollers

Running a Deep Neural Network on a microcontroller with 512KB of RAM requires extreme optimization.

●​ Quantization: Standard neural networks use 32-bit floating-point numbers. We convert these to 8-bit integers (INT8). This reduces the model size by 4x and speeds up inference, often with negligible loss in accuracy (<1%). ³⁵

●​ Pruning: We remove redundant connections (weights) in the neural network that contribute little to the output, creating "sparse" models that are faster to compute.

●​ CMSIS-NN: We utilize ARM's CMSIS-NN library, which provides hand-optimized assembly kernels for Convolution and Matrix Multiplication on Cortex-M processors. This allows us to squeeze every cycle of performance out of the hardware. ³⁷

5.3 Power Consumption Management

For sensors powered by batteries or Power-over-Ethernet (PoE), power efficiency is critical.

●​ Duty Cycling: The radar does not need to chirp continuously. We implement "inter-frame idle" states where the RF front-end is powered down.

●​ Hierarchical Wake-up: A low-power "presence detection" chirp runs continuously. Only when coarse motion is detected does the high-power "fall detection" deep learning model wake up to analyze the event. This "cascade" approach can extend battery life from days to months. ¹³

6. Overcoming Environmental Variance: The "Long Tail" of False Alarms

In the controlled environment of a laboratory, radar works perfectly. In a real home, there are ceiling fans, curtains blowing in the wind, and pets. These "false alarm generators" are the primary obstacle to commercial adoption. Veriprajna employs a multi-layered suppression strategy.

6.1 The "Ceiling Fan" Problem

A rotating fan generates a constant, high-frequency Doppler signature that can blind the CFAR algorithm or masquerade as a flailing limb.

●​ Solution: Microwave Noise Adaptive Processing. We train the AI to recognize the periodic, stationary-location signature of a fan. The system builds a "static map" of the room. If high Doppler velocity is consistently detected at a fixed $(x,y,z)$ coordinate (the ceiling), that specific cell is masked from the fall detection logic. The AI learns that "high velocity at $(x,y,z)_{fan}$ is normal". ³⁹

6.2 The "Pet" Problem

A large dog jumping off a sofa can generate a Doppler signature and point cloud trajectory dangerously similar to a falling human.

●​ Solution: RCS and Aspect Ratio Filtering.

○​ RCS (Radar Cross Section): While variable, the electromagnetic reflectivity of a dog is generally lower than that of an adult human.

○​ Geometric Classification: We analyze the bounding box of the point cloud. A human typically occupies a vertical column (Aspect Ratio $< 1$). A dog occupies a horizontal volume (Aspect Ratio $> 1$). Our classification models include a specific "Animal" class to explicitly filter these events. ³⁹

6.3 The "Curtain" Problem

Curtains moving in a draft create "ghost targets" that can trigger presence detection or false motion alerts.

●​ Solution: Zone Masking. During installation, the system allows for the definition of "Interference Zones" (windows, AC vents). The tracking algorithm (Extended Kalman Filter) increases the confidence threshold required to initiate a track in these zones. Additionally, the micro-Doppler signature of a curtain (low-frequency, sinusoidal oscillation) is distinct from human motion and is filtered by the deep learning classifier. ⁴¹

7. The Integration Ecosystem: Enterprise Connectivity

A sensor that detects a fall but cannot communicate it effectively is useless. For enterprise deployment in nursing homes and assisted living facilities, the sensor must integrate into the existing care ecosystem.

7.1 Nurse Call Systems (UL 1069)

The central nervous system of any care facility is the Nurse Call System (NCS) . This system is governed by UL 1069 ("Standard for Hospital Signaling and Nurse Call Equipment"), which mandates rigorous reliability, supervision, and failsafe operations. ⁴³

●​ Dry Contact / Relay Integration: The most robust and compatible method. Veriprajna sensors include an opto-isolated Solid State Relay (SSR) output. When a fall is detected, the relay closes. This connects to the "auxiliary" input of the legacy nurse call station on the wall (e.g., Rauland, Ascom, Hill-Rom). This triggers the standard nurse call light and pager system. It is simple, failsafe, and compatible with 90% of existing infrastructure. ⁴⁵

●​ High-Level API Integration: Modern NCS platforms support IP-based protocols. Veriprajna sensors can push JSON payloads via MQTT or REST APIs to a central server. This allows for richer data: instead of a generic "Room 302 Alarm," the nurse sees "Room 302: Fall Detected (High Confidence)" or "Room 302: Resident has not moved for 4 hours". ⁴⁶

7.2 Connectivity Protocols

●​ Wi-Fi / Ethernet: High bandwidth, suitable for pushing point cloud visualization to a nurse station (if permitted). Requires robust IT infrastructure.

●​ LoRaWAN: Low power, long range. Ideal for "check-in" messages (e.g., "Resident has not moved") and battery-operated sensors. However, latency capabilities must be evaluated for critical fall alerts. ⁴⁸

●​ Matter / Zigbee: Essential for smart home integration. For example, the sensor can trigger the lights to turn on automatically when a resident sits up in bed at night, acting as a preventative measure against falls. ⁴⁸

7.3 Compliance and Security Frameworks

●​ GDPR and HIPAA: Because mmWave radar does not capture biometric identifiers (faces, fingerprints) or recognizable images, it is intrinsically more compliant with GDPR and HIPAA than cameras. It processes "anonymous motion data." However, behavioral patterns (e.g., bathroom frequency) constitute health data (PHI) and must be protected. Veriprajna employs TLS 1.2+ encryption for all data in transit and AES-256 for data at rest. ⁶

●​ ISO 31700 (Privacy by Design): Veriprajna adheres to the ISO 31700 standard for consumer goods.

○​ Proactive: Privacy is engineered into the hardware (no microphone, no lens).

○​ Default: The highest privacy setting is the default.

○​ Lifecycle: Data minimization strategies ensure data is deleted when no longer

needed. ⁵⁰

8. Strategic Value and Future Horizons

Implementing Veriprajna’s Deep AI Radar solution is an investment, but the Return on Investment (ROI) is tangible and measurable.

8.1 The ROI of Dignity

●​ Direct Cost Avoidance: A single fall with injury costs a facility between $30,000 and $60,000 in medical costs, liability, and increased care requirements. ⁵²

●​ Indirect Cost Savings: Reducing "false alarms" reduces staff burnout and "alarm fatigue." When the system alerts, staff know it is real.

●​ ROI Calculation: Investing in a sensor system yields a positive ROI if it prevents just one hospitalization-level fall every 5 years. Studies show evidence-based fall prevention programs can deliver an ROI of over 500% ($5 saved for every $1 spent). ⁵²

8.2 Operational Efficiency through Analytics

Beyond emergency alerts, the system provides longitudinal analytics .

●​ Preventative Care: By tracking gait speed and activity levels over weeks, the system can detect the subtle decline that precedes a fall. "Mrs. Jones is walking 20% slower this week" is a powerful leading indicator that allows for intervention before an accident occurs.

●​ Staff Efficiency: Nurses no longer need to perform intrusive "rounds" just to check if a resident is in bed. The dashboard provides real-time presence status, allowing staff to focus on residents who actually need assistance. ⁵⁴

8.3 The Future: 6G and Sensor Fusion

The roadmap for radar is accelerating.

●​ 6G Sensing (ISAC): Future 6G networks will integrate sensing and communication (ISAC). The Wi-Fi router of the future will be the fall detection sensor, utilizing the ubiquitous radio waves already filling our homes. ²¹

●​ Multi-Modal Fusion: Combining mmWave radar with thermal imaging or acoustic sensors (for voice calls) will further enhance accuracy while maintaining privacy.

Conclusion

The transition from "surveillance" to "sensing" is the defining shift of the AgeTech decade. We have moved past the era where safety required a compromise on dignity. By replacing the camera lens with the mmWave antenna, and replacing the human monitor with Deep Edge AI, we resolve the conflict between safety and privacy.

Veriprajna is not just selling a sensor; we are providing the Deep AI infrastructure —the cleaned signals, the robust classification models, the false-alarm rejection logic, and the secure enterprise integration—that makes this promise a reality. We offer a solution that is physically incapable of "watching them naked," yet computationally capable of keeping them safe.

Safe. Secure. Private. This is the promise of Veriprajna.

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