Privacy preserving method based on Voronoi diagram in mobile crowd computing

Abstract

With the development of mobile smartphones equipped with a large number of sensor devices, mobile crowd computing has become the hotspot of current research. In the application, the publishers use the application platform to release the task and then select the appropriate users to participate in the task by bidding and collect their data, in which the users' identity, location, and other private information face the risk of disclosure. To solve the problem of privacy disclosure in mobile crowd computing, we propose a privacy preserving method based on Voronoi Polygon (PP-Voronoi). The users construct the Voronoi diagram to form a cloaked region hiding users identity and then calculate the anchor point of the cloaked region, by which the users communicate with the task publisher to achieve privacy protection of the cloaked region and K anonymity. The experimental results show that compared with other privacy preserving methods, PP-Voronoi method has better effectiveness of privacy protection and lower network traffic.

Voronoi diagram and Monte-Carlo simulation based finite element optimization for cost-effective 3D printing

“…Based upon statistics with mean vector and covariance matrix, a Voronoi diagram was proposed in [31]. A Voronoi diagram was constructed in [32] to form a cloaked region and calculate the anchor point of the cloaked region for privacy preservation. A window-vertex-sorted triangle propagation algorithm was proposed in [33] to construct geodesic based Voronoi diagrams.…”

A Privacy-Preserving Method Based on Server-Aided Reverse Oblivious Transfer Protocol in MCS

“…The authors in [14] proposed a privacy-aware framework, where the participants submitted their data to the server by adjusting the sensing range. Long et al [15] proposed a privacy preserving method based on Voronoi Polygon which formed a cloaked region to hide users identity by constructing the Voronoi diagram. However, the cloaking location of the uploaded data may lead to inaccurate checking results.…”

“…where λ 1 = λ 2 = 0. Proof: In order to prove the correctness of the proposition, we derive (14) and (15) in regard to P and r from (7) and (9) as follows:…”

“…∂L(P, r, λ 1 , λ 2 ) ∂r = Np + λ 2 − M α 2 α 3 P 2 e α 3 r (α 1 − α 2 e α 3 r ) 2 . (15) We set (14) and (15) to zero and calculate the result of the two formulas to derive the analytical solution of (10). The proof ends.…”