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Answer: Add noise to the salary data during the analysis
The correct answer is A because adding noise to the data during analysis is the core technique of differential privacy. This approach introduces controlled randomness that protects individual privacy while preserving the statistical properties of the dataset, allowing for accurate aggregate calculations like average salaries by city. The noise addition ensures that individual records cannot be re-identified while maintaining the overall distribution and reliability of the data. Option B (encryption) doesn't provide differential privacy as it either makes data unusable for analysis or requires decryption. Option C (removing data) violates the completeness requirement. Option D (converting to average) destroys the original distribution and statistical consistency, making it impossible to generate accurate aggregate reports that reflect the true data distribution.
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You have a dataset containing user salary information and need to generate an aggregate report showing average salaries by city while preserving individual privacy. The solution must maintain data accuracy, completeness, and reliability, and the aggregation must be statistically consistent with the original data distribution. You must return an approximate result instead of raw data.
Which differential privacy approach should you implement?
A
Add noise to the salary data during the analysis
B
Encrypt the salary data before analysis
C
Remove the salary data
D
Convert the salary data to the average column value