Neural Network Differential Privacy Parameter Selection
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Solution Overview
Problem
Current data obfuscation techniques for sensor data struggle to balance privacy protection and data utility, often sacrificing one for the other, as they fail to effectively protect sensitive information in sequential data without compromising its usefulness for downstream processing.
Innovation Solution
A computer system is configured to automatically select a differential privacy parameter in a neural network for data obfuscation, encoding input data into a latent space representation, applying noise based on a variance parameter, and decoding it to produce obfuscated output data, ensuring both privacy protection and data utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current data obfuscation techniques are applied to protect privacy in sequential sensor data, then privacy protection is improved, but data utility deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the differential privacy parameter ε (epsilon) based on the specific characteristics of the sequential data and the privacy requirements. Instead of using a fixed obfuscation level, the system modifies privacy parameters to achieve optimal balance between privacy protection and data utility for different data types and contexts.
Solution Approach 2:
The system implements dynamics by making the obfuscation process adaptive rather than static. The differential privacy parameter is selected dynamically based on data characteristics, query types, and privacy requirements, allowing the system to respond to changing conditions and maintain optimal performance across different scenarios.
2Reliability
If differential privacy parameter is manually selected, then privacy protection can be controlled, but system complexity and manual intervention requirements increase
Solution Approach 1:
The system applies self-service by implementing automated mechanisms that select appropriate differential privacy parameters without requiring manual intervention. The system autonomously evaluates data characteristics, privacy requirements, and utility considerations to automatically determine optimal parameter values, reducing complexity and enabling scalable deployment.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors the effectiveness of privacy protection and data utility, using this information to adjust and refine parameter selections. This closed-loop approach enables the system to learn from performance metrics and automatically optimize parameter choices over time.
Data Source
AI summary
Techniques for automatically selecting a differential privacy parameter in a neural network for data obfuscation are disclosed. In some embodiments, a computer system performs a method comprising: obtaining a privacy loss parameter of differential privacy; and training a neural network to perform data obfuscation operations, the training of the neural network comprising learning a variance parameter using the privacy loss parameter, the data obfuscation operations comprising: encoding input data into a latent space representation of the input data, the encoding of the input data comprising inferring latent space parameters of a latent space distribution based on the input data and sampling data from the latent space distribution, the latent space distribution being based on the variance parameter; and decoding the sampled data of the latent space representation into output data.