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Answer: Use date shifting with the context set to the unique ID of the test subject.
Option A is the optimal choice because it uses date shifting with context set to the unique ID of the test subject. This technique applies a consistent random shift to all dates for each individual test subject, preserving the exact intervals between dates (critical for clinical trial analysis) while obfuscating the actual calendar dates to prevent batch identification and bias. The community discussion strongly supports this approach with 100% consensus and upvoted comments emphasizing that date shifting preserves sequence and duration. Option D (FPE) is overkill for this use case and doesn't inherently preserve date intervals. Option B (TimePartConfig with random month/year) would destroy interval relationships. Option C (bucketing) would lose the precise interval data needed for clinical analysis.
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Your company is conducting clinical trials with study results stored in BigQuery. The medication administration intervals contain start and end dates that could identify specific batches and introduce bias if exposed. You need to obfuscate the actual start and end dates while preserving the exact interval duration for analysis.
What should you do?
A
Use date shifting with the context set to the unique ID of the test subject.
B
Extract the date using TimePartConfig from each date field and append a random month and year.
C
Use bucketing to shift values to a predetermined date based on the initial value.
D
Use the FFX mode of format preserving encryption (FPE) and maintain data consistency.
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