APLDP: Adaptive personalized local differential privacy data collection in mobile crowdsensing

Abstract

Local differential privacy (LDP) enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In the majority of current works, they assumed that the privacy preservation parameter is totally determined by the data collector and then dispatched to all participants in mobile crowdsensing. However, in the real world, due to different privacy preferences of participants, it is inelegant and unpromising for all participants to accept the same privacy preservation level during data collection. To address such issue, an adaptive personalized local differential privacy (APLDP) data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods (basic RAPPOR and k-RR) are adaptively chosen by the participants according to their different privacy preferences, as well as the best perturbation probability is adaptively adopted by the participants to perturb their private data. In such case, the adaptive boundary based on the minimum mean square error (MSE) is theoretically derived to allow the participant to adaptively choose the best perturbation method, and meanwhile, it allows the participant to adaptively choose the best perturbation probability. Then, two estimation mergence methods named the direct combination (DC) and the weighted combination (WC) are demonstrated to do efficient data aggregation. Experiments on both synthetic and real data sets show that the proposed APLDP scheme performs better than previous non-personalized proposals in terms of the MSE and the average error rate (AER), especially using WC estimation method.