An important aspect of such participatory applications is that they potentially collect sensitive sensor data pertaining to individuals. For example, GPS sensor readings can be utilized to infer the location (and sometimes path) of the individual. Such GPS sensor measurements can be shared within a larger community, for the application purpose alone, but at the same time it is necessary to ensure that an individual’s sensor data is not revealed to untrustworthy third parties. A problem that arises from the opt-in nature of crowd-sensing applications is when malicious individuals contribute erroneous sensor data (e.g., falsified GPS readings); hence, maintaining the integrity of sensor data collected is an important problem.

A popular approach to preserving privacy of data is anonymization [Sweeney, 2002], which deals with removing identifying information from the sensor data before sharing it with a third party. The drawback of such an approach is that anonymized GPS (or location) sensor measurements can still be used to infer the frequently visited locations of an individual and derive their personal details. Another approach relies on data perturbation, which adds noise to sensor data before sharing it with the community to preserve the privacy of an individual, is appropriate. Data perturbation approaches [Ganti, 2008] rely on adding noise in such a manner that the privacy of an individual is preserved, but at the same time it is possible to compute the statistics of interest with high accuracy (due to the nature of the noise being added).

Finally, the local analytics running on mobile devices only analyse data on that given device [Ganti, 2011]. Participatory applications rely on analysing the data from a collection of mobile devices, identifying spatio-temporal patterns. The patterns may help users build models and make predictions about the physical or social phenomena being observed. One example is the monitoring of water pollutants - an important aspect of environment protection is to build models to understand the dissemination of pollutants in the air, soil and water. By collecting large amount of data samples about pollutants, using specialized in this case, but still affordable and portable devices, one can not only monitor the concentration of pollution, but also detect patterns to model how the concentration evolves spatially and temporally as temperature, humidity and wind change. These models can help the environmental authority forecast and provide alerts to the public.