A Novel Self-adaptive Grid-partitioning Noise Optimization Algorithm Based on Differential Privacy

As the development of the big data and Internet, the data sharing of users that contains lots of useful information are needed more frequently. In particular, with the widespread of smart devices, a great deal of location-based data information has been generated. To ensure that service providers can supply a completely optimal quality of service, users must provide exact location information. However, in that case, privacy disclosure accident is endless. As a result, people are paying attention to how to protect private data with location information. Of all the solutions of this problem, the differential privacy theory is based on strict mathematics and provides precise definition and quantitative assessed methods for privacy protection, it is widely used in location-based application. In this paper, we propose a self-adaptive grid-partitioning algorithm based on differential privacy for noise enhancement, providing more rigorous protection for location information. The algorithm first partitions into a uniform grid for spatial two dimensions data and adds Laplace noise with uniform scale parameter in each grid, then select the grid set to be optimized and recursively adaptively add noise to reduce the relative error of each grid, and make a second level of partition for each optimized grid in the end. Firstly, this algorithm can adaptively add noise according to the calculated count values in the grid. On the other hand, the query error is reduced, as a result, the accuracy of partition count query (the query accuracy of the differential private two-dimensional publication data) can be improved. And it is proved that the adaptive algorithm proposed in this paper has a significant increase in data availability through experiments.