Privacy preserving big histogram aggregation for spatial crowdsensing

The popularity of mobile devices has far expanded the application scenarios of spatial crowdsensing, due to its ability to provide fine-grained multi dimensional sensor readings associated with location information. Privacy is one of the fundamental issues in crowdsensing, as these location-based sensor readings may reveal identities or activities of participants. In this paper, we adopts the state-of-art location privacy definition geo-indistinguishability, provide an efficient and effective privacy preserving histogram aggregation mechanism BFMM (Bit Flipping Matrix Mechanism) for fine-grained multi dimensional location-based data. Theoretical analyses and experimental results demonstrate the efficiency and effectiveness of our approach for fine-grained multidimensional location-based data. Specifically, the aggregation accuracy of our approach averagely outperforms existing methods by a factor of number of buckets in the histogram.