Privacy-Accuracy Trade-off Analysis for Learning Models
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Solution Overview
Problem
Existing data analysis methods face challenges in balancing privacy and accuracy when building learning models, particularly when dealing with sensitive data from multiple sources, as they often compromise privacy to maintain accuracy or incur high resource costs.
Innovation Solution
A system and method that evaluate and compare different privacy preservation techniques across dimensions like privacy level, accuracy, and resource cost, allowing users to choose an appropriate balance by identifying correlations between sensitive data fields and applying techniques such as data masking and differential privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy preservation techniques are applied to sensitive data fields, then privacy protection is improved, but resource cost increases
Solution Approach 1:
The system changes parameters by evaluating multiple privacy preservation techniques with different privacy levels and resource costs, allowing selection of optimal techniques based on specific requirements. The analyzer computes trade-offs by varying privacy parameters and resource allocation parameters to find the best balance point.
2Reliability
If privacy preservation techniques are applied to sensitive data fields, then privacy protection is improved, but model training accuracy may deteriorate
Solution Approach 1:
The system evaluates multiple privacy preservation techniques with varying privacy levels and measures their impact on model training accuracy. By changing privacy parameters and observing accuracy outcomes, the system identifies techniques that maintain acceptable accuracy while providing necessary privacy protection.
Solution Approach 2:
The system incorporates feedback loops where model training accuracy is measured after applying privacy preservation techniques, and this feedback is used to adjust technique selection and parameters. The analyzer uses accuracy measurements to refine recommendations for optimal privacy-accuracy balance.
3Reliability
If all sensitive data fields are protected with high privacy techniques, then privacy protection is improved, but computation time increases
Solution Approach 1:
The system applies local quality by evaluating and applying different privacy preservation techniques to different sensitive data fields based on their specific characteristics and sensitivity levels. Instead of uniform protection, the system tailors privacy techniques to local requirements of each data field, reducing overall computation time while maintaining necessary privacy protection.
Solution Approach 2:
The system implements partial action by selecting and applying privacy preservation techniques to only those data fields where they are most needed, rather than applying high-privacy techniques to all sensitive fields uniformly. The analyzer identifies which fields require stronger protection and applies appropriate techniques selectively.
Data Source
AI summary
In an approach for computing trade-offs between privacy and accuracy of data analysis on building a learning model, a processor receives a dataset for training a model. The dataset includes one or more pre-identified sensitive data fields. The processor determines a weight of each sensitive data field for the model. The processor evaluates resource cost of applying a privacy preservation technique to the one or more pre-identified sensitive data fields. The processor identifies correlation among the sensitive data fields. The processor presents a comparison of options for training the model, in terms of tradeoffs of accuracy for training the model and the resource cost of the privacy preservation technique.