Use Cases and Scenarios:
1. High-Dimensional Data Analysis: In scenarios where a large number of
queries are executed on high-dimensional data, basic composition
techniques may lead to overly conservative privacy bounds. Advanced
composition theorems like the Moments Accountant and Rényi Differential
Privacy can yield more precise guarantees that better capture the
nuanced behavior of the queries.
2. Complex Analysis Pipelines: In cases where multiple intermediate
computations are performed before final analysis results are obtained,
the accumulation of privacy loss can be complex. Advanced theorems help
in understanding how the privacy loss propagates through different
stages of the pipeline, resulting in improved overall privacy
guarantees.
3. Adaptive Queries: When queries adapt based on previous analysis
outcomes, the relationship between queries becomes intricate. Advanced
composition theorems can adapt to these dynamic scenarios, offering a
clearer understanding of how privacy loss accumulates in adaptive
settings.
4. Large-Scale Collaborations: In collaborative settings involving
multiple parties or data analysts, privacy amplification can become a
concern. Advanced composition theorems help maintain a strong privacy
guarantee in situations where multiple parties contribute queries.