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Differentially Private Mobile Crowd Sensing Considering Sensing Errors Sei, Yuichi Ohsuga, Akihiko © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered. MDPI 2020 eng journal article VoR https://uec.repo.nii.ac.jp/records/9665 10.3390/s20102785 https://doi.org/10.3390/s20102785 1424-8220 Sensors 20 10 2785 2785 https://uec.repo.nii.ac.jp/record/9665/files/sensors-20-02785-v2.pdf application/pdf 1.7 MB 2020-10-30