Date Jittering for Dataset De-identification
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Solution Overview
Problem
Existing methods for anonymizing dates in datasets suffer from interval attacks, loss of analytic value, disruption of incremental order integrity, and date drift, leading to an increased risk of re-identification.
Innovation Solution
The proposed solution involves a date shifting process that incorporates a secret jitter key to ensure repeatable and reversible date adjustments, maintaining incremental ordering integrity while preventing date drift and interval attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dates are anonymized by simple shifting or over-generalization, then re-identification risk is reduced, but analytic value is lost
Solution Approach 1:
The patent applies parameter changes by introducing a jitter value (random perturbation) to dates while constraining the jitter to be less than the inter-event interval. This transforms the date parameter slightly to protect privacy while preserving the temporal relationships needed for analysis, resolving the contradiction between risk reduction and analytic value preservation.
Solution Approach 2:
The patent uses partial action by applying only the necessary amount of perturbation (jitter < interval) rather than complete anonymization. This partial perturbation is sufficient to prevent simple interval attacks while maintaining enough temporal structure for meaningful analysis, avoiding excessive loss of analytic value.
2Reliability
If dates are over-generalized to protect privacy, then re-identification risk is reduced, but data quality for analytical studies degrades
Solution Approach 1:
Instead of over-generalization (e.g., grouping all dates into yearly buckets), the patent applies small parameter changes (jitter values) to individual dates. This maintains the precision of date values while introducing enough variation to protect privacy, preserving data quality for analytical studies that require precise temporal information.
3Reliability
If dates are perturbed to reduce re-identification risk, then privacy is protected, but interval information is lost
Solution Approach 1:
The patent changes date parameters by adding small jitter values that are strictly constrained to be less than the original inter-event interval. This ensures that the relative ordering and approximate spacing of events are preserved, maintaining interval information while still providing privacy protection through perturbation.
Solution Approach 2:
The patent applies partial perturbation (minimal jitter) rather than complete randomization. This partial action preserves the essential interval information needed for analysis while introducing sufficient variation to protect against interval attacks, avoiding excessive information loss.
Data Source
AI summary
Example systems and methods produce a data-jittered dataset having less than a predetermined risk of re-identification. An example method includes partitioning a dataset into a plurality of time-separated groups based on dates associated with a plurality of data records in the dataset, the plurality of time-separated groups comprising a first group associated with a first time period and a second group associated with a second time period, determining a first jitter amount for the first group, jittering dates in the first group by the first jitter amount, determining a second jitter amount for the second group, and jittering dates in the second group by the second jitter amount. The second jitter amount is different from the first jitter amount and preserves ordering of the dates associated with the plurality of data records in the dataset.