Addressing trade-offs between privacy, utility, and
computational complexity14:
1. Privacy vs. Utility: Introducing more noise for stronger privacy
often reduces the utility of analysis results. Striking the right
balance is crucial to ensure meaningful insights are extracted from the
data.
2. Privacy vs. Computational Complexity: More noise can increase the
computational complexity of differential privacy mechanisms.
Organizations need to consider the trade-off between stronger privacy
and resource requirements.
3. Privacy vs. Accuracy: Stricter privacy mechanisms might lead to less
accurate analysis results. Finding the optimal compromise between
privacy and accuracy is a key challenge.
4. Utility vs. Computational Complexity: Implementing advanced
composition techniques might require complex calculations, impacting
computational efficiency. Deciding when to use advanced techniques
depends on the value they provide relative to the added complexity.
In conclusion, implementing composition algorithms faces practical
challenges due to complexity, adaptive scenarios, and interactions with
differential privacy mechanisms. Careful consideration is needed to
identify scenarios where composition techniques might not be directly
applicable. Addressing trade-offs between privacy, utility, and
computational complexity is crucial for achieving the right balance in
data analysis, ensuring meaningful insights while preserving individual
privacy.