Open Access

ARTICLE

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

Shuyu Li¹, Guozheng Zhang^{1, *}

1 School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China.

* Corresponding Author: Guozheng Zhang. Email: .

Computers, Materials & Continua 2020, 63(1), 223-241. https://doi.org/10.32604/cmc.2020.07499

Received 28 May 2019; Accepted 19 July 2019; Issue published 30 March 2020

Abstract

With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size (the number of workers). Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform. In the stage of data aggregation, DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise (including both added noise of sensing data and the system noise in the sensing process). Through using optimal estimation of noisy aggregated sensing data, the platform can finally gain better utility of aggregated data while preserving workers’ privacy. Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.

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Citations