A differential privacy noise dynamic allocation algorithm for big multimedia data

An advanced differential privacy algorithm is proposed in this paper to solve the problem of non-uniformity faced with two-dimensional big multimedia data, such as images. Traditional privacy-preserving algorithms partition a spatial data space into grids and then add noise to each grid at same scale. Such a treatment increases relative errors and reduces accuracy. To address this issue, a differential privacy noise dynamic allocation algorithm is proposed based on the standard deviation circle radius hereafter referred to as SDC-DP algorithm. In our proposed algorithm, the intensity of privacy-preserving needs is defined by the divergence of each grid which is calculated by the standard deviation circle radius. The different scale of noise is mixed dynamically into count query results for each grid on the privacy-preserving needs. Experimental results show that the SDC-DP can effectively reduce the relative errors and improve accuracies, compared to the state-of-the-art techniques.