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  5. FileBounty: fair data exchange
 
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FileBounty: fair data exchange
File(s)
2008.11362.pdf (614.87 KB)
Accepted version
Author(s)
Janin, Simon
Qin, Kaihua
Mamageishvili, Akaki
Gervais, Arthur
Type
Conference Paper
Abstract
Digital contents are typically sold online through centralized and custodian marketplaces, which requires the trading partners to trust a central entity. We present FileBounty, a fair protocol which, assuming the cryptographic hash of the file of interest is known to the buyer, is trust-free and lets a buyer purchase data for a previously agreed monetary amount, while guaranteeing the integrity of the contents. To prevent misbehavior, FileBounty guarantees that any deviation from the expected participants' behavior results in a negative financial payoff; i.e. we show that honest behavior corresponds to a subgame perfect Nash equilibrium. Our novel deposit refunding scheme is resistant to extortion attacks under rational adversaries. If buyer and seller behave honestly, FileBounty's execution requires only three on-chain transactions, while the actual data is exchanged off-chain in an efficient and privacypreserving manner. We moreover show how FileBounty enables a flexible peer-to-peer setting where multiple parties fairly sell a file to a buyer.
Date Issued
2020-10-22
Date Acceptance
2020-10-01
Citation
2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 2020, pp.357-366
URI
http://hdl.handle.net/10044/1/87936
URL
https://ieeexplore.ieee.org/document/9229692
DOI
https://www.dx.doi.org/10.1109/eurospw51379.2020.00056
Publisher
IEEE
Start Page
357
End Page
366
Journal / Book Title
2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Lucerne University Applied Sciences and Arts
Identifier
https://ieeexplore.ieee.org/document/9229692
Grant Number
Kaihua Qin
Source
2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)
Publication Status
Published
Start Date
2020-09-07
Finish Date
2020-09-11
Coverage Spatial
Genoa, Italy
Date Publish Online
2020-10-22
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