You have a dataset that contains salary information for users. You plan to generate an aggregate salary report that shows average salaries by city. Privacy of individuals must be preserved without…

Adding calibrated noise to the salary data during analysis is the defining mechanism of differential privacy. The injected noise masks individual contributions so that no single person's salary can be inferred, while the aggregate statistics - such as average salary by city - remain statistically consistent with the original distribution, satisfying the requirements for accuracy, completeness, and privacy simultaneously.

Encryption protects data from unauthorized access at rest or in transit but does not prevent statistical re-identification once the data is decrypted and analyzed; it is an access control mechanism, not a privacy-preserving analysis technique.

Removing salary data would destroy the completeness and reliability of the dataset, directly violating the requirement that accuracy, completeness, and reliability must not be impacted.

Replacing all salary values with the column average eliminates the statistical distribution entirely, producing a uniform dataset that is no longer statistically consistent with the original data and cannot support meaningful aggregate analysis.