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Secure Nearest Neighbor Query on Crowd-Sensing Data.

Ke Cheng1, Liangmin Wang2, Hong Zhong3

  • 1School of Computer Science and Technology, Anhui University, Hefei 230601, China. chengke@ahu.edu.cn.

Sensors (Basel, Switzerland)
|September 27, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a secure nearest neighbor query scheme for crowd-sensing data. It protects data and query privacy against untrusted cloud servers and malicious insiders in multi-owner, multi-user environments.

Keywords:
collusion attackcrowd-sensingprivacy-preservationsecure nearest neighborsecure two-party computation

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Area of Science:

  • Computer Science
  • Data Security
  • Cloud Computing

Background:

  • Nearest neighbor queries are crucial for location-based services.
  • Crowd-sensing data introduces challenges due to numerous, untrusted data owners and limited terminal capabilities.
  • Existing secure query schemes struggle with the Multi Owners and Multi Users (MOMU) paradigm.

Purpose of the Study:

  • To propose a novel secure nearest neighbor query scheme tailored for crowd-sensing environments.
  • To address data confidentiality and query privacy concerns in outsourced cloud data.
  • To develop a robust solution against collusion between cloud servers and data participants.

Main Methods:

  • Utilizes a proxy server architecture.
  • Employs secure two-party computation protocols.
  • Incorporates a secure Voronoi diagram algorithm.

Main Results:

  • The proposed scheme effectively preserves data confidentiality and query privacy.
  • It demonstrates strong resistance against collusion attacks.
  • Evaluations show a superior balance between security and query performance.

Conclusions:

  • The presented scheme offers a secure and efficient solution for nearest neighbor queries in crowd-sensing data.
  • It is well-suited for the MOMU cloud environment.
  • The approach enhances trust and performance in decentralized data systems.