Specialized worker profiles of crowdsourcing platforms may contain a large amount of identifying and possibly sensitive personal information (e.g., personal preferences, skills, available slots, available devices) raising strong privacy concerns. This led to the design of privacy-preserving crowdsourcing platforms, that aim at enabling efficient crowdsourcing processes while providing strong privacy guarantees even when the platform is not fully trusted. We propose a demonstration of the PKD algorithm, a privacy-preserving space partitioning algorithm dedicated to enabling secondary usages of worker profiles within privacy-preserving crowdsourcing platforms by combining differentially private perturbation with additively-homomorphic encryption. The demonstration scenario showcases the PKD algorithm by illustrating its use for enabling requesters tune their tasks according to the actual distribution of worker profiles while providing sound privacy guarantees.