As communications-enabled devices are becoming more and more ubiquitous, it becomes easier to track the movements of individuals through the radio signals broadcasted by their devices. While there is a strong interest for physical analytics platforms to leverage this information for many purposes, this tracking also threatens the privacy of individuals. To solve this issue, we propose a privacy-preserving solution for collecting aggregate mobility patterns while at the same time satisfying the strong guarantee of ε-differential privacy. More precisely, we introduce a sanitization mechanism for efficient, privacy-preserving and non-interactive approximate distinct counting for physical analytics based on perturbed Bloom filters. We also extend and generalize previous approaches for estimating distinct count of events and joint events (i.e., intersection, and more generally tout of -n cardinalities). Finally, we experimentally evaluate our approach and compare it to previous ones on a real dataset.