Get by how much you pay: A novel data pricing scheme for data trading

【Author】 Lu, Yu; Wang, Jingyu; Liu, Lixin; Yang, Hanqing

【Source】INFORMATION PROCESSING & MANAGEMENT

【影响因子】7.466

【Abstract】As a crucial step in promoting data sharing, data trading can stimulate the development of the data economy. However, the current data trading market primarily focuses on satisfying data owners' interests, overlooking the demands of data requesters. Ignoring the demands of data requesters may lead to a loss of market competitiveness, customer loss, and missed business opportunities while damaging reputation and innovation capabilities. Therefore, in this paper, we introduce a novel pricing mechanism named Get By How Much You Pay (GHMP) based on compressed sensing technology and game theory to address pricing problems according to data requesters' demands. This scheme employs a dictionary matrix as the sparse basis matrix in compressed sensing. The quality of this matrix directly affects the precision with which the requester can reconstruct the data. If the requester requires higher-precision data, the corresponding payment will also increase accordingly so as to realize the pricing method based on the requester's demands. A game pricing method is proposed to address the final pricing and purchasing issues between the data requester and the data owner by utilizing an authorized smart contract as an intermediary. As an entity participating in the game, the smart contract can only receive a higher transaction fee if it successfully assists the data requester and data owner in completing the pricing. Therefore, it strives to establish more reasonable prices for both parties during the trading process to obtain profits. The experimental results demonstrate that this gamebased approach assists the data requester and owner in achieving optimal data pricing, thereby satisfying the maximization of interests for both parties.

【Keywords】Data pricing; Compressed sensing; Dictionary matrix; Game theory; Privacy preservation

【发表时间】2024 NOV

【收录时间】2024-08-20

【文献类型】理论模型

【主题类别】

区块链应用-虚拟经济-数据资产交易