The average mobile device includes several sensors as a standard feature. Moreover, it roams with its owner, and can be used to collect context information on their behalf. It is often vital to collect data in order to create realistic models that might help us understand and predict the world. However, sharing personal data increases the chance of a user's privacy being compromised by revealing their identity. In this thesis we show that most of the sensor data on a device should be handled with caution due to their potential to be a privacy threat and propose solutions for service self-provisioning for measuring location tracking information through unaided triangulation, and location context by using cell ID traces. When data absolutely needs to reach a third party, we show that opportunistic mixing strategies can be effective in anonymizing the source of the data.