{"type":"ScholarlyArticle","title":"Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain","authors":[{"name":"Naman Goel","slug":"naman-goel","given":"Naman","family":"Goel","affiliation":"Swiss Federal Institute of Technology , Lausanne ("},{"name":"Cyril van Schreven","slug":"cyril-van-schreven","given":"Cyril","family":"van Schreven","affiliation":"Swiss Federal Institute of Technology , Lausanne ("},{"name":"Aris Filos-Ratsikas","slug":"aris-filos-ratsikas","given":"Aris","family":"Filos-Ratsikas","affiliation":"University of Liverpool , UK"},{"name":"Boi Faltings","slug":"boi-faltings","given":"Boi","family":"Faltings","affiliation":"Swiss Federal Institute of Technology , Lausanne (; EPFL)"}],"abstract":"Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the financial transactions. In this paper, we identify two biggest challenges in building decentralized, trustless and transparent oracles. The first challenge is acquiring correct information about the real-world events without relying on a trusted information provider. We show how a peer-consistency incentive mechanism can be used to acquire truthful information from an untrusted and self-interested crowd, even when the crowd has outside incentives to provide wrong informations. The second is a system design and implementation challenge. For the first time, we show how to implement a trustless and transparent oracle in Ethereum. We discuss various non-trivial issues that arise in implementing peer-consistency mechanisms in Ethereum, suggest several optimizations to reduce gas cost and provide empirical analysis.","doi":"10.24963/ijcai.2020/635","url":"https://ijcai.org/proceedings/2020/635","pdf":"https://ijcai.org/proceedings/2020/0635.pdf","markdown":"https://ijcai.org/proceedings/2020/635.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/635","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":635,"pages":{"first":4604,"last":4610},"keywords":["Foundation for AI in FinTech: Modeling economic mechanisms and social welfare","AI for lending: AI for smart contracts","Foundation for AI in FinTech: Modeling economic incentives","Foundation for AI in FinTech: AI for financial infrastructure","Foundation for AI in FinTech: General","AI for lending: AI for blockchain"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25579/file_25579.m3u8","length_sec":297,"provider":"hls"}],"references":[{"position":1,"raw":"[Adler et al., 2018] John Adler, Ryan Berryhill, Andreas Veneris, Zissis Poulos, Neil Veira, and Anastasia Kastania. Astraea: A decentralized blockchain oracle. In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (Green- Com) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pages 1145-1152. IEEE, 2018.","title":"Astraea: A decentralized blockchain oracle","authors":"John Adler, Ryan Berryhill, Andreas Veneris, Zissis Poulos, Neil Veira, Anastasia Kastania","year":"2018","venue":"IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (Green-Com) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Agarwal et al., 2017] Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah. Peer prediction with heterogeneous users. In Proceedings of the 18th ACM Conference on Economics and Computation (EC), 2017.","title":"Peer prediction with heterogeneous users","authors":"Arpit Agarwal, Debmalya Mandal, David C Parkes, Nisarg Shah","year":"2017","venue":"Proceedings of the 18th ACM Conference on Economics and Computation (EC)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[An et al., 2019] Baoyi An, Mingjun Xiao, An Liu, Guoju Gao, and Hui Zhao. Truthful crowdsensed data trading based on reverse auction and blockchain. In International Conference on Database Systems for Advanced Applica- tions, pages 292-309. Springer, 2019.","title":"Truthful crowdsensed data trading based on reverse auction and blockchain","authors":"An","year":"2019","venue":"International Conference on Database Systems for Advanced Applications","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chakraborty and Das, 2016] Mithun Chakraborty and San- may Das. Trading on a rigged game: Outcome manipula- tion in prediction markets. In IJCAI, 2016.","title":"Mithun Chakraborty and Sanmay Das. Trading on a rigged game: Outcome manipulation in prediction markets","authors":"Das Chakraborty","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2011] Yiling Chen, Xi Alice Gao, Rick Gold- stein, and Ian A Kash. Market manipulation with outside incentives. In AAAI, 2011.","title":"Market manipulation with outside incentives","authors":"Yiling Chen, Alice Xi, Rick Gao, Ian A Goldstein, Kash","year":"2011","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dasgupta and Ghosh, 2013] Anirban Dasgupta and Arpita Ghosh. Crowdsourced judgement elicitation with endoge- nous proficiency. In Proceedings of the 22nd international conference on World Wide Web. ACM, 2013.","title":"Crowdsourced judgement elicitation with endogenous proficiency","authors":"Ghosh Dasgupta, Anirban Dasgupta, Arpita Ghosh","year":"2013","venue":"Proceedings of the 22nd international conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Faltings and Radanovic, 2017] Boi Faltings and Goran Radanovic. Game theory for data science: Eliciting truthful information. Synthesis Lectures on Artificial Intelligence and Machine Learning, 11(2):1-151, 2017.","title":"Boi Faltings and Goran Radanovic. Game theory for data science: Eliciting truthful information","authors":"Radanovic Faltings","year":"2017","venue":"Synthesis Lectures on Artificial Intelligence and Machine Learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Freeman et al., 2017] Rupert Freeman, Sébastien Lahaie, and David M Pennock. Crowdsourced outcome determi- nation in prediction markets. In AAAI, 2017.","title":"Crowdsourced outcome determination in prediction markets","authors":"Rupert Freeman, Sébastien Lahaie, David M Pennock","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gao et al., 2016] Alice Gao, James R Wright, and Kevin Leyton-Brown. Incentivizing evaluation via limited ac- cess to ground truth: Peer-prediction makes things worse. In 2nd Workshop on Algorithmic Game Theory and Data Science at EC 2016., 2016.","title":"Incentivizing evaluation via limited access to ground truth: Peer-prediction makes things worse","authors":"Alice Gao, James R Wright, Kevin Leyton-Brown","year":"2016","venue":"2nd Workshop on Algorithmic Game Theory and Data Science at EC","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Goel and Faltings, 2019a] Naman Goel and Boi Faltings. Deep bayesian trust: A dominant and fair incentive mech- anism for crowd. Proceedings of the AAAI Conference on Artificial Intelligence, 2019.","title":"Deep bayesian trust: A dominant and fair incentive mechanism for crowd","authors":"Faltings Goel, Naman Goel, Boi Faltings","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Goel and Faltings, 2019b] Naman Goel and Boi Faltings. Personalized peer truth serum for eliciting multi-attribute personal data. In 35th Conference on Uncertainty in Arti- ficial Intelligence, Tel Aviv (UAI 2019). AUAI, 2019.","title":"Personalized peer truth serum for eliciting multi-attribute personal data","authors":"Faltings Goel, Naman Goel, Boi Faltings","year":"2019","venue":"35th Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Goel et al., 2020] Naman Goel, Aris Filos-Ratsikas, and Boi Faltings. Peer-prediction in the presence of outcome dependent lying incentives. Proceedings of the 29th In- ternational Joint Conference on Artificial Intelligence (IJ- CAI), 2020.","title":"Peer-prediction in the presence of outcome dependent lying incentives","authors":"Naman Goel, Aris Filos-Ratsikas, Boi Faltings","year":"2020","venue":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJ-CAI)","doi":null,"url":null,"internal_url":"/proceedings/2020/18"},{"position":13,"raw":"[Jurca and Faltings, 2005] Radu Jurca and Boi Faltings. En- forcing truthful strategies in incentive compatible reputa- tion mechanisms. In International Workshop on Internet and Network Economics, pages 268-277. Springer, 2005.","title":"Enforcing truthful strategies in incentive compatible reputation mechanisms","authors":"Faltings Jurca, Radu Jurca, Boi Faltings","year":"2005","venue":"International Workshop on Internet and Network Economics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kong et al., 2019] Yuqing Kong, Yiping Ma, and Yifan Wu. Securely trading unverifiable information without trust. arXiv preprint arXiv:1903.07379, 2019.","title":"Securely trading unverifiable information without trust","authors":"Yuqing Kong, Yiping Ma, Yifan Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2018] Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jia-Nan Liu, Yang Xiang, and Robert H Deng. Crowdbc: A blockchain-based decentral- ized framework for crowdsourcing. IEEE Transactions on Parallel and Distributed Systems, 30(6):1251-1266, 2018.","title":"Crowdbc: A blockchain-based decentralized framework for crowdsourcing","authors":"Ming Li, Jian Weng, Anjia Yang, Wei Lu, Yue Zhang, Lin Hou, Jia-Nan Liu, Yang Xiang, Robert H Deng","year":"2018","venue":"IEEE Transactions on Parallel and Distributed Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu and Chen, 2017] Yang Liu and Yiling Chen. Machine- learning aided peer prediction. In Proceedings of the 2017 ACM Conference on Economics and Computation, pages 63-80. ACM, 2017.","title":"Machinelearning aided peer prediction","authors":"Chen ; Liu, Yang Liu, Yiling Chen","year":"2017","venue":"Proceedings of the 2017 ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lu et al., 2018] Yuan Lu, Qiang Tang, and Guiling Wang. Zebralancer: Private and anonymous crowdsourcing sys- tem atop open blockchain. In 2018 IEEE 38th Inter- national Conference on Distributed Computing Systems (ICDCS), pages 853-865. IEEE, 2018.","title":"Zebralancer: Private and anonymous crowdsourcing system atop open blockchain","authors":"Yuan Lu, Qiang Tang, Guiling Wang","year":"2018","venue":"IEEE 38th International Conference on Distributed Computing Systems (ICDCS)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Miller et al., 2005] Nolan Miller, Paul Resnick, and Richard Zeckhauser. Eliciting informative feedback: The peer- prediction method. Management Science, 51(9):1359- 1373, 2005.","title":"Eliciting informative feedback: The peerprediction method","authors":"Nolan Miller, Paul Resnick, Richard Zeckhauser","year":"2005","venue":"Management Science","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Prelec, 2004] Dražen Prelec. A bayesian truth serum for subjective data. Science, 306(5695):462-466, 2004.","title":"Dražen Prelec. A bayesian truth serum for subjective data","authors":"Prelec","year":"2004","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Radanovic et al., 2016] Goran Radanovic, Boi Faltings, and Radu Jurca. Incentives for effort in crowdsourcing using the peer truth serum. ACM Transactions on Intelligent Sys- tems and Technology (TIST), 7(4):48, 2016.","title":"Incentives for effort in crowdsourcing using the peer truth serum","authors":"Goran Radanovic, Boi Faltings, Radu Jurca","year":"2016","venue":"ACM Transactions on Intelligent Systems and Technology (TIST)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ryan, 2017] Danny Ryan. Calculating costs in ethereum contracts, 2017.","title":"Calculating costs in ethereum contracts","authors":"Danny Ryan","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Shnayder et al., 2016] Victor Shnayder, Arpit Agarwal, Rafael Frongillo, and David C Parkes. Informed truthful- ness in multi-task peer prediction. In Proceedings of the 2016 ACM Conference on Economics and Computation, pages 179-196. ACM, 2016.","title":"Informed truthfulness in multi-task peer prediction","authors":"Victor Shnayder, Arpit Agarwal, Rafael Frongillo, David C Parkes","year":"2016","venue":"Proceedings of the 2016 ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Waggoner and Chen, 2014] Bo Waggoner and Yiling Chen. Output agreement mechanisms and common knowledge. In Second AAAI Conference on Human Computation and Crowdsourcing, 2014.","title":"Output agreement mechanisms and common knowledge","authors":"Chen ; Waggoner, Bo Waggoner, Yiling Chen","year":"2014","venue":"Second AAAI Conference on Human Computation and Crowdsourcing","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Witkowski and Parkes, 2012] Jens Witkowski and David C Parkes. Peer prediction without a common prior. In Pro- ceedings of the 13th ACM Conference on Electronic Com- merce, pages 964-981. ACM, 2012.","title":"Peer prediction without a common prior","authors":"Parkes Witkowski, Jens Witkowski, David C Parkes","year":"2012","venue":"Proceedings of the 13th ACM Conference on Electronic Commerce","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xiong and Xiong, 2019] Wei Xiong and Li Xiong. Smart contract based data trading mode using blockchain and machine learning. IEEE Access, 2019.","title":"Smart contract based data trading mode using blockchain and machine learning","authors":"Wei Xiong, Li Xiong","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zheng et al., 2014] Zibin Zheng, Yilei Zhang, and Michael R Lyu. Investigating qos of real-world web services. IEEE transactions on services computing, 7(1):32-39, 2014.","title":"Investigating qos of real-world web services","authors":"Zheng","year":"2014","venue":"IEEE transactions on services computing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}