Case Studies illustrating Application and Impact:
1. Healthcare Research: Consider a scenario where a healthcare
institution releases aggregated statistics about patient conditions. The
initial differential privacy mechanism ensures individual privacy.
However, a researcher wishes to apply a statistical technique to further
analyse the aggregated data. The impact of this post-processing step on
privacy must be evaluated to guarantee that the privacy guarantees
remain intact.
2. Social Media Analytics: In the context of analyzing social media
data, renewal-based composition can be beneficial. If sentiment analysis
is performed periodically to gauge public sentiment over time,
renewal-based composition can refresh privacy parameters at each
analysis point. This helps maintain privacy guarantees as new data
arrives and analyses are repeated.
3. Epidemiological Studies: In the field of epidemiology, researchers
might repeatedly analyse data to track disease patterns. Renewal-based
composition can help prevent the gradual loss of privacy protection over
time. By refreshing privacy parameters periodically, the analyses
maintain a consistent level of privacy, even in the face of repeated
queries.
4. Machine Learning Aggregation: In a collaborative machine learning
setting, multiple parties might contribute models for aggregation.
Post-processing could involve aggregating models and refining the
aggregated model. The combined effect of the aggregation mechanism and
post-processing on privacy must be assessed to ensure privacy
preservation.