Differential Privacy Clustering for Data Anonymity
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Solution Overview
Problem
Existing k-anonymity algorithms fail to simultaneously process categorical and numerical attributes of datasets effectively, leading to over-generalization and increased information loss, and are unable to maintain rigorous privacy guarantees while preserving data anonymity.
Innovation Solution
Applying differential privacy on clustered data using a combination of clustering algorithms and epsilon-delta differential privacy operations, which allows for flexible information preservation and rigorous privacy guarantees, enabling the handling of both categorical and numerical attributes without over-generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If k-anonymity algorithms are applied to anonymize datasets, then data anonymity is improved, but information loss increases due to over-generalization
Solution Approach 1:
The patent segments the dataset into multiple clusters based on similarity metrics, where each cluster represents a group of records with comparable attributes. This segmentation allows for more precise anonymization within each cluster while preserving distinguishing features between clusters, thereby reducing over-generalization and information loss compared to traditional k-anonymity approaches that treat all records uniformly.
Solution Approach 2:
The patent applies differential privacy with carefully calibrated epsilon parameters locally to each cluster rather than uniformly across the entire dataset. This local quality approach allows for finer control over the trade-off between privacy and information retention in different regions of the data, preserving more information in less sensitive areas while maintaining strong privacy guarantees where needed.
2Adaptability or versatility
If clustering algorithms are applied to process categorical and numerical attributes, then processing capability is improved, but computational complexity increases
Solution Approach 1:
The patent develops a unified clustering framework that simultaneously handles both categorical and numerical attributes through a shared similarity metric and clustering algorithm. This multi-functional approach eliminates the need for separate processing pipelines for different data types, reducing overall computational complexity while maintaining the ability to process diverse attribute types effectively.
3Reliability
If differential privacy operation is applied to clustered data, then privacy guarantees are improved, but query accuracy may deteriorate
Solution Approach 1:
The patent applies differential privacy mechanisms in advance during the cluster formation process rather than as a post-processing step. By incorporating privacy preservation into the clustering algorithm itself, the method achieves rigorous privacy guarantees while minimizing the impact on query accuracy, as the privacy noise is integrated into the cluster structure rather than added separately to query results.
Data Source
AI summary
Embodiments for data anonymity by a processor. A dataset may be transformed into an anonymous dataset by applying a differential privacy operation and a clustering operation to the dataset.