Anonymization Granularity Control via Tree-Structured Dictionary
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Solution Overview
Problem
Existing data anonymization methods face challenges in balancing the granularity of anonymization to minimize data loss while ensuring that the probability of individual identification remains below a certain threshold, leading to either excessive record deletion or significant information loss.
Innovation Solution
An information processing device that classifies records into sets based on masking target items using a tree-structured dictionary, calculates the number of records and their ratios, and displays these ratios to support the determination of appropriate anonymization granularity, thereby minimizing record deletion while preserving information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is anonymized with fine granularity to minimize information loss per record, then information quality is improved, but the number of records to be deleted increases
Solution Approach 1:
The system dynamically adjusts anonymization granularity by allowing users to select different hierarchy levels in the tree-structured dictionary. This enables flexible control over the degree of masking applied to each attribute, transforming a static anonymization process into a dynamic one that adapts to different data loss and record deletion trade-offs.
Solution Approach 2:
The invention changes the parameter of anonymization granularity by introducing a hierarchy level parameter in the tree-structured dictionary. By adjusting this parameter, the system can control the level of detail retained in anonymized data, thereby managing the balance between information preservation and record deletion.
2Quantity of substance
If data is anonymized with rough granularity to minimize record deletion, then the number of records retained is improved, but information loss per record increases
Solution Approach 1:
The system allows dynamic adjustment of anonymization granularity through user-selectable hierarchy levels. This enables the same system to produce both rough and fine granularity anonymization results depending on the desired balance between record retention and information preservation.
Solution Approach 2:
By changing the hierarchy level parameter in the tree-structured dictionary, the system can control the degree of information abstraction. Higher hierarchy levels retain more records but with coarser granularity, while lower levels provide finer detail at the cost of more record deletions.
3Reliability
If more records are deleted to achieve k-anonymization compliance, then privacy protection is improved, but data analysis accuracy degrades
Solution Approach 1:
The system dynamically balances privacy protection and analysis accuracy by allowing users to adjust the anonymization granularity. This enables optimization of the k-anonymization parameter and hierarchy level selection to achieve the desired balance between privacy compliance and data utility.
Data Source
AI summary
With respect to an information processing device which anonymizes data composed of records including one or more items through statistical processing, the information processing device includes a memory, and a processor configured to classify respective records constituting the data into one or more first sets, based on masking target items, a dictionary, and a selected hierarchy level indicating a hierarchy level selected in the tree structure for each of the masking target items, 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 number of records of each of the one or more second sets and a ratio of records belonging to each of the one or more second sets to the records, and display the ratio in each hierarchy level of each of the masking target items.