Enterprise AI Analysis
Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation
Authored by: YI YIN, GUANGQUAN ZHANG, HUA ZUO, and JIE LU, University of Technology Sydney, Australia
Machine learning relies on high-quality datasets, but sharing raw data risks privacy breaches like membership inference attacks (MIA). Existing privacy-preserving techniques often degrade data utility. This paper introduces a novel bilevel optimization framework to address this, balancing data utility (upper-level) and privacy preservation (lower-level). It leverages a Riemannian Variational Autoencoder (RVAE) and curvature-guided perturbations to identify and protect vulnerable data points, ensuring high-quality synthetic data generation and strong MIA resistance. Our method outperforms traditional techniques in sample quality, diversity, and privacy protection for downstream tasks.
Executive Summary: Balancing Privacy & Utility for Enterprise AI
In an era where data is paramount but privacy is critical, our solution offers a robust framework for publishing sensitive datasets without compromise. By integrating advanced machine learning with geometric privacy principles, we empower enterprises to unlock the full potential of their data while mitigating the most sophisticated privacy threats.
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Lowest Average MIA Success Rate
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Highest Average Test Accuracy
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Lowest Average FID Score
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Novel Bilevel Optimization Framework
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Privacy-Utility Trade-off
Methodology
Experimental Results
Enterprise Applications
Achieving Optimal Balance
Our framework significantly reduces membership inference attack vulnerability while maintaining high data utility, outperforming existing methods in balancing privacy and utility. The bilevel optimization allows for a precise, dynamic trade-off, adapting to dataset characteristics and specific privacy requirements.
53.11%
Lowest Average MIA Success Rate Achieved
Bilevel Optimization Workflow
Our novel bilevel optimization framework guides data perturbation by leveraging intrinsic data manifold curvature, ensuring both privacy preservation and data utility. This sophisticated approach enables targeted protection of vulnerable data points without significant degradation of overall dataset quality.
Enterprise Process Flow
Input Dataset (Raw Data)
→
Encoder (Latent Variable Z)
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Curvature Estimator (Vulnerability Scoring)
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Geodesic Obfuscator (Targeted Perturbation)
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Decoder (Reconstructed Output)
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Output Dataset (Privacy-Preserving)
Performance Across Methods (Average)
A comprehensive comparison against baseline methods demonstrates our model's superior performance across key privacy and utility metrics, achieving the lowest MIA rate and best overall quality. This table presents the average performance across all evaluated datasets.
| Method | MIA Success Rate (↓) | Test Acc (↑) | FID Score (↓) | IS Score (↑) |
|---|---|---|---|---|
| Original | 60.65% | 94.00% | / | 2.7535 |
| Ours | 53.11% | 88.15% | 201.9559 | 2.4612 |
| DPDM | 56.40% | 85.25% | 417.1978 | 2.1842 |
| VAEGAN-DP | 58.19% | 72.33% | 676.5227 | 2.2901 |
| K-anonymity | 54.64% | 77.90% | 349.9903 | 2.2213 |
| Blur | 56.35% | 74.64% | 446.5279 | 2.1617 |
| Pixelation | 55.67% | 86.28% | 1069.1132 | 1.6665 |
Mitigating Risk in Medical Imaging (OCTMNIST)
Our method's robust performance on the OCTMNIST dataset showcases its potential for securing highly sensitive medical imaging data, a critical application for enterprise AI.
OCTMNIST: Enhanced Privacy for Sensitive Medical Data
The OCTMNIST dataset, with its intricate medical imaging features and inherent vulnerabilities, presents a significant challenge for privacy-preserving data publication. Traditional DP methods often struggle, with VAEGAN-DP achieving MIA success rates above 68% and DPDM remaining less effective. Our framework, however, demonstrates robust performance on OCTMNIST, significantly reducing attack success rates to 52.26% (compared to original 64.75%) while preserving essential features. This is achieved through structured, curvature-guided perturbations that ensure controlled movement away from high-curvature regions, which are often indicative of unique or sensitive patterns in medical images. This capability highlights our method's potential for real-world applications in sensitive domains like healthcare, where both data utility and stringent privacy are paramount.
Our method achieved a MIA success rate of 52.26% on OCTMNIST, a significant reduction from the original 64.75%, demonstrating its effectiveness in critical, sensitive domains.
Estimate Your Enterprise AI ROI
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Estimated Annual Savings
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Annual Hours Reclaimed
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Strategic AI Implementation Roadmap
Our phased approach ensures a smooth, secure, and effective integration of curvature-guided privacy into your data publication workflows.
Phase 1: Discovery & Data Manifold Analysis
Initial assessment of your existing datasets, privacy requirements, and downstream utility needs. Deployment of the Riemannian Variational Autoencoder (RVAE) to map and analyze the intrinsic geometry of your data manifold.
Phase 2: Curvature-Guided Perturbation Engine Setup
Training and calibration of the curvature estimator to identify vulnerable, high-curvature regions within your data. Configuration of the geodesic obfuscator for targeted, privacy-preserving perturbations.
Phase 3: Bilevel Optimization & Model Refinement
Execution of the bilevel optimization framework, iteratively refining the RVAE-GAN and geodesic obfuscator. This phase focuses on achieving the optimal balance between data utility and resistance to membership inference attacks.
Phase 4: Validation & Enterprise Integration
Rigorous evaluation of the generated datasets using MIA success rates, classification accuracy, FID, and IS scores. Seamless integration of the privacy-preserving publication pipeline into your existing enterprise data management systems.
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