Hierarchical composition in multi-level and multi-agent scenarios11:
Hierarchical composition deals with privacy guarantees in settings involving multiple levels of data analysts or agents, each with varying degrees of access and analysis capabilities. In these scenarios, privacy needs to be preserved across multiple levels, ensuring that lower-level agents cannot extract sensitive information about individuals.
This concept is particularly relevant in applications such as collaborative machine learning, where different parties contribute data or models. Hierarchical composition ensures that even if certain agents have access to aggregated or partially processed data, they cannot compromise the privacy of individual contributors.