Choreographic programming provides a high-level abstraction for writing distributed computations while ensuring deadlock-freedom by construction. HasChor, a Haskell-based choreographic programming framework, offers a practical implementation of these ideas but faces limitations in handling an arbitrary number of participants and optimizing communication patterns. In this work, we enhance HasChor’s expressiveness and efficiency by introducing \emph{multiply-located values}, allowing choreographies to distribute and collect data across multiple participants in a structured manner. Additionally, we refine HasChor’s \emph{branching mechanism} through a lightweight static analysis, selectively propagating branching decisions only to relevant participants, thereby reducing communication overhead. To strengthen theoretical guarantees, we establish a \emph{formal semantics} for HasChor and our extensions, proving deadlock-freedom. Finally, we demonstrate the practicality of our enhancements by implementing a \emph{data clean room protocol} within HasChor. Our contributions improve the applicability of choreographic programming to cloud-based secure data collaborations, making it a stronger candidate for real-world deployments.