Privacy-Preserving Location-Based Task Allocation

Abstract

Chapter 3 discusses privacy-preserving task allocation based on task content. In mobile crowdsensing (MCS), since tasks are usually closely related to locations, data requesters may wish to find sensing users located within a certain geometric range to complete the task. Therefore, this chapter will study the privacy-preserving task allocation based on geometric range. Existing privacy-preserving geometric range query schemes either cannot achieve query in arbitrary geometric range or rely on trusted data centers to process location data. To address the challenges, this chapter proposes a privacy-preserving task allocation scheme GPTA that supports arbitrary geometric range queries. Specifically, this chapter firstly proposes GPTA-L with linear query efficiency based on the techniques of polynomial fitting and random matrix multiplication. In order to further improve query efficiency, this chapter studies the query history of data requesters and designs a nonlinear query efficiency scheme GPTA-F based on geometric properties. Security analysis proves that the proposed scheme can effectively preserve the user’s location privacy and query privacy. Experiments based on the mobile crowdsensing applications verify that the proposed scheme has high computational and communication efficiency.