Task-Aware Encoder-Decoder Privacy for Multi-Dimensional Data
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Solution Overview
Problem
Conventional local differential privacy (LDP) approaches for data anonymization in machine learning systems are task-agnostic, leading to significant performance loss by indiscriminately injecting noise into all data attributes, which is inefficient and degrades task accuracy.
Innovation Solution
Implementing a task-aware privacy preservation approach using an encoder-decoder network that learns and anonymizes a task-relevant latent representation of user data, controlling noise injection based on attribute relevance to the specific task, thereby improving task accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional LDP approaches inject noise into all data attributes according to a given privacy budget, then privacy guarantee is maintained, but task accuracy deteriorates significantly
Solution Approach 1:
The patent applies local quality by differentiating the noise injection strategy across different data attributes based on their task relevance. Instead of uniform noise injection, the system selectively applies noise to attributes according to their importance for the specific task, thereby preserving privacy where needed while maintaining accuracy where data quality is critical.
Solution Approach 2:
The patent changes the parameter of noise injection from a uniform approach to a differentiated approach. By modifying the noise injection parameters based on attribute relevance to the task, the system achieves both privacy preservation and task accuracy improvement, resolving the contradiction between these two requirements.
2Ease of operation
If conventional LDP approaches use task-agnostic noise injection, then implementation simplicity is maintained, but data utility for specific tasks deteriorates
Solution Approach 1:
The patent introduces dynamics by making the noise injection strategy adaptive to the specific task. The system dynamically adjusts which attributes receive noise and at what intensity, based on task relevance. This dynamic approach maintains implementation simplicity while significantly improving data utility for specific tasks compared to static task-agnostic approaches.
3Reliability
If noise is injected into all data attributes to ensure privacy, then privacy budget is consumed, but information relevant to the task is lost
Solution Approach 1:
The patent extracts the task-relevant information from the full data attribute set before applying noise. By identifying and separating the attributes that are most relevant to the task, the system can apply noise only to less critical attributes or apply it differently, thereby preserving task-relevant information while still providing privacy protection.
Solution Approach 2:
The patent applies local quality by differentiating the noise injection strategy across different data attributes based on their task relevance. Instead of uniform noise injection, the system selectively applies noise to attributes according to their importance for the specific task, thereby preserving privacy where needed while maintaining accuracy where data quality is critical.
Data Source
AI summary
A method performed by at least one processing device in an illustrative embodiment comprises applying an input data sample to an encoder of an encoder-decoder network to generate a corresponding latent representation, combining the latent representation from the encoder with noise, applying the combined latent representation and noise to a decoder of the encoder-decoder network to generate an estimated data sample, and outputting the estimated data sample. Respective sets of parameters of the encoder and decoder of the encoder-decoder network are configured based at least in part on an iterative optimization process utilizing a task loss determined from a task function that relates the input data sample to a task output. A given iteration of the iterative optimization process illustratively generates an estimated task output from the estimated data sample, determines a loss measure using the estimated task output, and adjusts one or more parameters using the loss measure.