Faithfulness Limitations:
Complexity: Ensuring record-level faithfulness can be computationally intensive, especially for large datasets. This may impact the scalability of the scheme and the efficiency of data generation.
Subjectivity: The definition of faithfulness relies on the cost function, which may be subjective and vary based on stakeholder preferences. This subjectivity can introduce bias and affect the interpretation of the results.
Trade-off with Privacy: Striving for high faithfulness may compromise differential privacy guarantees, as stricter matching requirements could lead to increased privacy risks.
Face Privacy Limitations:
Loss of Information: Enforcing face privacy by removing unique records may result in the loss of valuable information. Unique records could contain important insights that are crucial for certain analyses.
Data Distortion: Applying dataset projections to achieve face privacy may distort the data distribution, affecting the accuracy of statistical analyses. This distortion could impact the overall data quality and utility.
User Expectations: Stakeholders' expectations of face privacy may not align with the technical requirements of differential privacy. Balancing these expectations while maintaining privacy guarantees can be challenging.
Addressing these limitations requires careful consideration of the trade-offs between data utility, privacy protection, and stakeholder expectations.