DOI: 10.5281/zenodo.16418741
title: PrivAware

description: # PRISM-DECODE: Privacy-Preserving Scene Understanding for Public Health Surveillance

## Overview

This repository implements **PRISM (Privacy-Regularized Inference and Structure Modeling)** and **DECODE (Decentralized Compression and Obfuscation for Differential Exposure)** — a novel framework for **privacy-preserving AI-driven public health surveillance**. Our approach integrates privacy constraints directly into model architecture and training workflows to mitigate privacy leakage while maintaining strong scene understanding performance.

## Motivation

AI-based public health surveillance systems often analyze sensitive visual data from public environments, raising concerns about privacy, consent, and ethical governance. Traditional methods (e.g., anonymization, obfuscation) are insufficient against modern threats such as:- Membership inference attacks- Attribute inference- Latent feature reconstruction

PRISM-DECODE addresses these threats by embedding privacy preservation into the AI model’s computational structure and training dynamics.

## Key Contributions

- **PRISM Framework**:   - Decomposes latent space into utility- and sensitive-related subspaces.  - Uses adversarial training and orthogonal constraints to disentangle sensitive information.  - Suppresses mutual information between latent representations and sensitive attributes.

- **DECODE Protocol**:  - Dynamically regulates gradient and feature exposure using entropy-aware dropout and perturbation.  - Enforces distributional alignment with a reference prior and constrains batch-wise statistical leakage.  - Promotes invariance under sensitive attribute perturbations via auxiliary encoders.

- **Sensitivity-Aware Modules**:   - Estimate and suppress latent unit responsiveness to protected attributes.  - Apply selective attention masking at the feature level.

## Architecture

- **Encoder-Decoder Backbone** with U-Net-style skip connections.- **Dilformer Blocks**: Multi-scale dilated convolutions for high-resolution feature extraction.- **Adversarial Discriminator**: Attempts to decode sensitive attributes; used to train obfuscation.- **Entropy-based Regularizers**: Applied to both feature magnitude and training gradients.

(Refer to *Figure 1–4 in the paper* for detailed architectural diagrams.)

## Datasets

Evaluated across 4 challenging public datasets:- **Masked Faces Dataset**: Occlusion-aware facial identity recognition.- **Avenue Dataset**: Anomaly detection in surveillance videos.- **DukeMTMC**: Multi-camera person re-identification.- **Market-1501 / MSMT17**: Benchmark person re-ID with occlusions and lighting variation.

## Results

PRISM-DECODE demonstrates state-of-the-art performance in both utility and privacy metrics:

| Dataset            | Accuracy | F1 Score | AUC     ||--------------------|----------|----------|---------|| Masked Faces       | 93.42%   | 91.08%   | 94.10%  || Avenue             | 92.27%   | 89.88%   | 93.76%  || DukeMTMC           | 93.11%   | 90.94%   | 94.28%  || RE-IDENTIFICATION  | 96.20%   | 93.90%   | 95.71%  |

Compared to leading baselines (CLIP, ViT, BLIP, T5), our model shows consistent improvement, especially in privacy-sensitive benchmarks.

## How It Works

The loss function integrates multiple objectives:

```pythonL_total = L_pred + L_priv + λ_align * L_align + λ_inv * L_inv + λ_env * ExposurePenalty