A Robust Spatio-Temporal Verification Protocol for Blockchain

Massive Spatio-temporal data is increasingly collected in a variety of domains including supply chain. The authenticity as well as the security of such data is usually a concern due to the requirement of trust in centralised systems. Blockchain technology has come to the forth recently and offers ways for trustless and reliable storage and processing of data. However, current blockchain proposals either do not support spatial data or make simplifying assumptions such as ‘trusted’ servers to process spatio-temporal data. We presume that the notion of ‘trust’ in a blockchain is too strong an assumption and propose a robust spatio-temporal verification protocol for the blockchain. In this work, we present a novel practical proof-of-location protocol on top of a permissioned blockchain. The protocol is instrumented by the implementation of an access control model and utilises a set of verification rules to create and verify spatio-temporal data points. We also propose a threat-to-validity model to evaluate the robustness of the verification protocol. The applicability and practicality of the protocol is demonstrated by the implementation of a supply chain case study as a proof-of-concept.

[1]  Sarah Underwood,et al.  Blockchain beyond bitcoin , 2016, Commun. ACM.

[2]  Srdjan Capkun,et al.  Are We Really Close? Verifying Proximity in Wireless Systems , 2017, IEEE Security & Privacy.

[3]  Xiaofei Wang,et al.  Cloud-enabled wireless body area networks for pervasive healthcare , 2013, IEEE Network.

[4]  Nancy A. Lynch,et al.  Impossibility of distributed consensus with one faulty process , 1985, JACM.

[5]  Sanjay Jha,et al.  I Am Alice, I Was in Wonderland: Secure Location Proof Generation and Verification Protocol , 2016, 2016 IEEE 41st Conference on Local Computer Networks (LCN).

[6]  Prasant Mohapatra,et al.  STAMP: Enabling Privacy-Preserving Location Proofs for Mobile Users , 2016, IEEE/ACM Transactions on Networking.

[7]  Miguel Oom Temudo de Castro,et al.  Practical Byzantine fault tolerance , 1999, OSDI '99.

[8]  Jie Yang,et al.  Detection and Localization of Multiple Spoofing Attackers in Wireless Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.

[9]  Gérard Lachapelle,et al.  GPS Vulnerability to Spoofing Threats and a Review of Antispoofing Techniques , 2012 .

[10]  Cory Searcy,et al.  Assessing sustainability in the supply chain: A triple bottom line approach , 2015 .

[11]  H. Stadtler Supply Chain Management: An Overview , 2015 .

[12]  Qiang Qu,et al.  Renovating blockchain with distributed databases: An open source system , 2019, Future Gener. Comput. Syst..

[13]  Arif Ur Rahman,et al.  Trajectory Mining Using Uncertain Sensor Data , 2018, IEEE Access.

[14]  Sébastien Gambs,et al.  PROPS: A PRivacy-Preserving Location Proof System , 2014, 2014 IEEE 33rd International Symposium on Reliable Distributed Systems.

[15]  Christian S. Jensen,et al.  Efficient Online Summarization of Large-Scale Dynamic Networks , 2016, IEEE Transactions on Knowledge and Data Engineering.

[16]  Dong Xuan,et al.  A mobile phone-based physical-social location proof system for mobile social network service , 2016, Secur. Commun. Networks.

[17]  Qiang Qu,et al.  ChainMOB: Mobility Analytics on Blockchain , 2018, 2018 19th IEEE International Conference on Mobile Data Management (MDM).

[18]  Paul C. van Oorschot,et al.  Location verification on the Internet: Towards enforcing location-aware access policies over Internet clients , 2014, 2014 IEEE Conference on Communications and Network Security.

[19]  Jean-Pierre Hubaux,et al.  SecureRun: Cheat-Proof and Private Summaries for Location-Based Activities , 2016, IEEE Transactions on Mobile Computing.