Real-world examples showcasing challenges and benefits:
1. Healthcare Research Collaboration: In a collaborative healthcare
research scenario, different hospitals contribute patient data to train
a model. Adaptive data analysis is necessary to fine-tune the model, but
the privacy of individual patient data must be preserved. Hierarchical
composition ensures that each hospital’s contribution remains private,
even as the model adapts.
2. Online Recommender Systems: In an online platform, user preferences
are analyzed to provide recommendations. Adaptive data analysis is
essential for refining these recommendations, but it introduces privacy
challenges. By utilizing hierarchical composition, the platform ensures
that individual user profiles remain private, even as the recommendation
algorithm adapts to user feedback.
3. Financial Fraud Detection: Adaptive data analysis is crucial for
detecting evolving financial fraud patterns. However, privacy concerns
arise when analyzing transaction data. Hierarchical composition ensures
that different financial institutions’ data remains confidential, even
as the analysis adapts to emerging fraud tactics.
4. Genomic Research: In genomics, researchers collaborate to analyse
genetic data for insights. Adaptive analysis is necessary to identify
potential correlations between genetic markers and diseases.
Hierarchical composition guarantees that individual genetic information
remains protected, even as collaborative analyses adapt.