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MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus
Faheem Zafar1, Abid Khan2, Saif Ur Rehman Malik3
1Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 42000, Pakistan.
Sensors (Basel, Switzerland)
|August 10, 2021
Summary
MobChain introduces a decentralized consensus protocol to secure location proof systems (LPS). This blockchain-based approach prevents three-way collusion, enhancing the reliability of location-based services (LBS) on smart devices.
Area of Science:
- Computer Science
- Cybersecurity
- Distributed Systems
Background:
- Smart devices necessitate reliable geolocation information for location-based services (LBS).
- Existing witness-oriented location proof systems (LPS) are vulnerable to three-way collusion attacks.
- Tampering of location proof by users compromises the integrity of LBS.
Purpose of the Study:
- To introduce a novel decentralized consensus protocol, MobChain, to address three-way collusion in LPS.
- To enhance the security and reliability of geolocation identification in smart devices.
- To provide a robust architecture for secure location provenance.
Main Methods:
- Developed MobChain, a decentralized consensus protocol utilizing a private blockchain.
- Implemented a P2P network for distributed consensus on witness and location authority selection.
- Leveraged persistent provenance data on the blockchain for tamper-proof location records.
Main Results:
- MobChain effectively mitigates three-way collusion attacks by decentralizing participant selection.
- The blockchain architecture ensures persistent and immutable location provenance data.
- Prototype implementation demonstrates computational efficiency and high availability.
Conclusions:
- MobChain offers a secure and practical solution for location proof systems, overcoming limitations of existing schemes.
- Decentralized decision-making in MobChain enhances the trustworthiness of LBS.
- The proposed system significantly improves the security of geolocation identification against manipulation.
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