Data Anonymization via Dynamic Hierarchy Adjustment
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Solution Overview
Problem
Existing data anonymization methods, such as k-anonymization, often result in high data loss rates, which degrade the accuracy of data analysis, especially when cross-analysis is performed, as they require deleting records to maintain anonymity, leading to a trade-off between data loss and analysis precision.
Innovation Solution
An information processing device that classifies records using a tree-structured dictionary to select appropriate hierarchy levels for masking items, calculating the number of records and ratios, and iteratively adjusting these levels to minimize data loss while ensuring k-anonymity, thereby maintaining analysis accuracy through statistical processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If records are deleted to achieve k-anonymization, then data anonymity is improved, but data loss rate increases and analysis accuracy degrades
Solution Approach 1:
The patent changes the parameter of hierarchy level for masking target items dynamically. By adjusting the hierarchy level (e.g., from detailed to abstract levels in a tree-structured dictionary), the system finds an optimal balance where data anonymity is achieved while minimizing data loss. This parameter change allows flexible control over the trade-off between anonymity and data utility.
Solution Approach 2:
The patent implements a dynamic process where the hierarchy level is not fixed but adjusted iteratively based on calculated ratios. The system dynamically modifies the masking strategy by changing hierarchy levels until a predetermined condition is met, enabling adaptive optimization of both anonymity and data preservation.
2Loss of information
If item values are abstracted to reduce record deletion, then data loss rate decreases, but cross-analysis accuracy degrades
Solution Approach 1:
The patent dynamically adjusts the hierarchy level based on calculated ratios of records at different abstraction levels. This dynamic adjustment ensures that abstraction is applied only when necessary and at the appropriate level, preserving cross-analysis accuracy while minimizing data loss.
Solution Approach 2:
The system calculates the ratio of records at different hierarchy levels and uses this feedback to determine whether to adjust the masking level. This feedback mechanism ensures that abstraction decisions are data-driven, maintaining cross-analysis accuracy by avoiding excessive abstraction.
3Loss of information
If hierarchy levels are adjusted to minimize data loss, then data utility is improved, but calculation complexity increases
Solution Approach 1:
The patent segments the data processing into distinct steps: calculating the number of records at each hierarchy level, calculating ratios, comparing with predetermined conditions, and adjusting hierarchy levels accordingly. This segmentation makes the complex optimization process manageable and systematic.
Solution Approach 2:
The system automatically calculates ratios and determines optimal hierarchy levels without requiring manual intervention for each adjustment. The automated feedback loop performs the complex calculations and adjustments itself, reducing the burden on users while maintaining data utility.
Data Source
AI summary
An information processing device, anonymizing data composed of records including one or more items through statistical processing, includes a memory, and a processor configured to acquire a data set from a server, classify respective records constituting the data into one or more first sets, based on masking target items, a dictionary, and a selected hierarchy level, classify the respective records into one or more second sets with respect to a number of records belonging to each of the one or more first sets, and calculate a ratio of records belonging to each of the one or more second sets to the records constituting the data, calculate an index value regarding merged data, change the hierarchy level based on the ratio of records, priority set in advance, and the index value, and create a statistically processed record by statistically processing records belonging to a same first set.