SCAFT: A Scalable Crowd-Assisted Fair Trading Protocol

The online data marketplace is experiencing rapid growth and ensuring fairness between trading participants is essential as they do not trust each other due to the nature of the Internet. With the emergence of blockchain technology, it is possible to guarantee online trading fairness without leveraging a single trusted authority. In existing solutions, data are encrypted, and the decryption keys are exchanged with cryptocurrencies atomically through a Hash Time-Locked Contract (HTLC). Unfortunately, existing protocols share an underlying assumption that requires a direct, secure channel between each pair of users (namely, each pair of seller and buyer) since some consensus must be secretly reached before deploying the HTLC. However, this assumption of pair-wise secure channels decreases the practicality of existing solutions because such channels are not widely available for normal users on the Internet, especially for the sellers, since they need to create a secure channel with every single buyer. Besides, the use of pair-wise secure channels makes the seller the only entity that can provide correct answers to smart contracts, which makes it harder to facilitate the trading process and to further improve the protocols' scalability. In this paper, we address the above concerns by proposing a novel scalable fair trading protocol called SCAFT that eliminates direct user communications and supports crowd-assisted selling. We analyzed our protocol's security and proved that SCAFT is secure against any polynomial adversaries considered in this paper. Evaluation results indicate that although our protocol causes extra gas consumption for both the seller and the buyer, the communication costs between them are further reduced.

Authors

Institutions

Publication Details

Published
2024-08-19
DOI
https://doi.org/10.1109/blockchain62396.2024.00065
Primary Topic
Auction Theory and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SCAFT: A Scalable Crowd-Assisted Fair Trading Protocol

Wenyi Tang, Changhao Chenli, Shankha Shubhra Mukherjee, Taeho Jung
Auction Theory and Applications
article

SCAFT: A Scalable Crowd-Assisted Fair Trading Protocol

Wenyi Tang, Changhao Chenli, Shankha Shubhra Mukherjee, Taeho Jung
article en

Abstract

The online data marketplace is experiencing rapid growth and ensuring fairness between trading participants is essential as they do not trust each other due to the nature of the Internet. With the emergence of blockchain technology, it is possible to guarantee online trading fairness without leveraging a single trusted authority. In existing solutions, data are encrypted, and the decryption keys are exchanged with cryptocurrencies atomically through a Hash Time-Locked Contract (HTLC). Unfortunately, existing protocols share an underlying assumption that requires a direct, secure channel between each pair of users (namely, each pair of seller and buyer) since some consensus must be secretly reached before deploying the HTLC. However, this assumption of pair-wise secure channels decreases the practicality of existing solutions because such channels are not widely available for normal users on the Internet, especially for the sellers, since they need to create a secure channel with every single buyer. Besides, the use of pair-wise secure channels makes the seller the only entity that can provide correct answers to smart contracts, which makes it harder to facilitate the trading process and to further improve the protocols' scalability. In this paper, we address the above concerns by proposing a novel scalable fair trading protocol called SCAFT that eliminates direct user communications and supports crowd-assisted selling. We analyzed our protocol's security and proved that SCAFT is secure against any polynomial adversaries considered in this paper. Evaluation results indicate that although our protocol causes extra gas consumption for both the seller and the buyer, the communication costs between them are further reduced.

Vol. 15
Indiana Institute of Technology (US), University of Notre Dame (US)
National Science Foundation, National Aeronautics and Space Administration
Openalex Percentile: Top 81%
Auction Theory and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

SCAFT: A Scalable Crowd-Assisted Fair Trading Protocol — Wenyi Tang, Changhao Chenli, et al. · (2024) | TGRS Research Map | TGRS