  This paper introduces MapComp, a novel view-based framework to facilitate
join-group-aggregation (JGA) queries for collaborative analytics. Through
specially crafted materialized view for join and novel design of
group-aggregation (GA) protocols, MapComp removes duplicated join workload and
expedites subsequent GA, improving the efficiency of JGA query execution. To
support continuous data updates, our materialized view offers
payload-independence feature and brings in significant efficiency improvement
of view refreshing with free MPC overhead. This feature also allows further
acceleration for GA, where we devised multiple novel protocols that outperform
prior works. Notably, our work represents the first endeavor to expedite secure
collaborative JGA queries using materialized views. Our experiments demonstrate
a significant advantage of MapComp, achieving up to a 2189.9x efficiency
improvement compared to the non-view based baseline when executing queries
eight times.
