Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the-completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with − 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure-completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.