When URLs containing application parameters are posted in public settings privacy can be compromised if the those arguments contain personal or tracking data. To this end we describe a privacy aware link shortening service that attempt to strip sensitive and non-essential parameters based on difference algorithms and human feedback. Our implementation, CleanURL, allows users to validate our automated logic and provides them with intuition about how these otherwise opaque arguments function. Finally, we apply CleanURL over a large Twitter URL corpus to measure the prevalence of such privacy leaks and further motivate our tool.