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Orthos: A Trustworthy AI Framework for Data Acquisition

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Engineering Multi-Agent Systems (EMAS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12589))

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Abstract

Information acquisition through crowdsensing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires efficient information elicitation mechanisms to guarantee that the collected data are accurately acquired and reported. Such mechanisms can be implemented via smart contracts on blockchain to enable privacy and trust. In this work we develop Orthos, a blockchain-based trustworthy framework for spontaneous location-based crowdsensing queries without assuming any prior knowledge about them. We employ game-theoretic mechanisms to incentivize agents to report truthfully and ensure that the information is collected at the desired location while ensuring the privacy of the agents. We identify six necessary characteristics for information elicitation mechanisms to be applicable in spontaneous location-based settings and implement an existing state-of-the-art mechanism using smart contracts. Additionally, as location information is exogenous to these mechanisms, we design the Proof-of-Location protocol to ensure that agents gather the data at the desired locations. We examine the performance of Orthos on Rinkeby Ethereum testnet and conduct experiments with live audience.

In Greek, Orthos means correct and accurate.

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Notes

  1. 1.

    https://bitcoin.org/.

  2. 2.

    https://ethereum.org/.

  3. 3.

    https://ripple.com.

  4. 4.

    https://docs.soliditylang.org/en/v0.7.5/.

  5. 5.

    Keccak is a versatile cryptographic function. Best known as a hash function, it nevertheless can also be used for authentication, encryption and pseudo-random number generation. For more information, please refer to https://keccak.team/keccak.html.

  6. 6.

    https://rinkeby.etherscan.io/.

  7. 7.

    https://infura.io/docs.

  8. 8.

    https://www.web3labs.com/web3j.

  9. 9.

    https://docs.soliditylang.org/en/v0.4.24/contracts.html/events.

  10. 10.

    https://provable.xyz.

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Author information

Authors and Affiliations

  1. International Institute of Information Technology Hyderabad, Hyderabad, India

    Moin Hussain Moti & Sujit Gujar

  2. The Hong Kong University of Science and Technology, Hong Kong, China

    Dimitris Chatzopoulos & Pan Hui

  3. University of Helsinki, Helsinki, Finland

    Pan Hui

  4. Ecole Polytechnique Federale de Lausanne, 1015, Lausanne, Switzerland

    Boi Faltings

Authors
  1. Moin Hussain Moti
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  2. Dimitris Chatzopoulos
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  3. Pan Hui
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  4. Boi Faltings
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  5. Sujit Gujar
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Corresponding author

Correspondence to Moin Hussain Moti .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Universita di Torino, Torino, Torino, Italy

    Cristina Baroglio

  2. Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil

    Jomi F. Hubner

  3. School of Information Management, Victoria University of Wellington, Wellington, New Zealand

    Michael Winikoff

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Moti, M.H., Chatzopoulos, D., Hui, P., Faltings, B., Gujar, S. (2020). Orthos: A Trustworthy AI Framework for Data Acquisition. In: Baroglio, C., Hubner, J.F., Winikoff, M. (eds) Engineering Multi-Agent Systems. EMAS 2020. Lecture Notes in Computer Science(), vol 12589. Springer, Cham. https://doi.org/10.1007/978-3-030-66534-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-66534-0_7

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