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SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications.

Nsikak Pius Owoh1, Manmeet Mahinderjit Singh1

  • 1School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia.

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Summary
This summary is machine-generated.

SenseCrypt enhances mobile crowd sensing security by encrypting user location data using K-means and certificateless aggregate signcryption. This framework protects sensitive information, ensuring privacy and data integrity in mobile sensing applications.

Keywords:
Internet of Thingsdata annotationdata compressionmessage queuing telemetry transport protocolmobile crowd sensingsecurity and privacysigncryption

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Area of Science:

  • Computer Science
  • Information Security
  • Mobile Computing

Background:

  • Mobile crowd sensing (MCS) leverages ubiquitous mobile devices for data collection, offering advantages over traditional wireless sensor networks.
  • Despite its potential, MCS faces significant security and privacy challenges, particularly concerning the protection of sensitive user location data.
  • The dynamic nature of Global Positioning System (GPS) sensor states in smartphones exacerbates these location privacy concerns.

Purpose of the Study:

  • To propose a novel framework, SenseCrypt, for securing sensitive location information in mobile crowd sensing.
  • To address the critical need for robust privacy-preserving mechanisms in MCS environments.
  • To ensure the confidentiality, integrity, and non-repudiation of user data within MCS systems.

Main Methods:

  • Developed SenseCrypt, a framework employing the K-means algorithm for data clustering and a certificateless aggregate signcryption (CLASC) scheme.
  • Integrated spatial coding for efficient data compression and Message Query Telemetry Transport (MQTT) as the messaging protocol.
  • Implemented automated annotation and signcryption of sensitive location data.

Main Results:

  • The SenseCrypt framework demonstrated low computational cost and minimal communication overhead.
  • Evaluations confirmed the framework's robustness against various security threats, including privileged insider, replay, and forgery attacks.
  • The proposed solution effectively provides confidentiality, integrity, and non-repudiation for MCS data.

Conclusions:

  • SenseCrypt offers a viable solution for enhancing the security and privacy of location data in mobile crowd sensing.
  • The framework's efficiency and resilience make it suitable for practical deployment in MCS applications.
  • By addressing key security vulnerabilities, SenseCrypt promotes trustworthy data collection and user privacy in mobile sensing ecosystems.