Data Boundary and Data Pricing Based on the Shapley Value

Abstract

Data pricing is to price data as asset to promote the healthy development of data share, exchange, and reuse. However, the uncertain value index and neglection of interactivity lead to information asymmetry in the transaction process. A perfect data pricing system and a well-designed data market can widely promote data transactions. We take a three-agent data market in this paper, the data owner who provides the data record, the model buyer who is interested in buying machine learning (ML) model instances, and the broker who interacts between the data owner and the model buyer. Two interaction scenarios are defined. In scenario 1, we introduce the Shapley value (SV) to measure values of data records fairly and construct a revenue optimization problem based on the sum of the SV for data boundary. The optimal solution is obtained by calculating their derivatives. In scenario 2, we utilize market research and construct a revenue maximization (RM) problem to price the ML models. Further,An Integer Linear Programming for the RM problem (RM-ILP) process is proposed to transform the RM problem into an equivalent integer linear programming (ILP) problem and solve it with the Gurobi solver. Finally, we conduct extensive experiments, which validate that the RM-ILP process can provide high revenue to the broker and high affordability to the model buyers compared to the benchmarks.

[Retracted] Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross‐Border Data Flow Governance

“…The generation of cross-border data has both a technical and contemporary background. From the technical side, the leap-forward changes in digital technology, the explosive growth of mobile terminals, inexpensive storage devices, high-speed broadband, and cloud computing are the prerequisites for cross-border data generation [ 10 ]. Digital technology involves all aspects of data generation, collection, and analysis.…”

Towards a taxonomy for business capabilities determining data value

“…This data value can be of social-ecological, economical [25,26], functional and/or symbolic nature [10,26] with the purpose of adding a measurable business value [13]. Thereby, data value is determined by a multitude of value drivers [27,28], underlying theories [29][30][31][32][33], as well as frameworks [34,35].…”

“…The analysis of the SLR ndings illustrates that two main groups particularly stood out with regard to their frequency of mentions with reference to past and future research. The rst, most frequently and deeply discussed group includes all stakeholders involved in determining the data value and is therefore summarized under the value determination stakeholder dimension in this taxonomy [29,48]. The second relevant group is derived particularly from the relevance for future research and can be subsumed in the dimension value auditing stakeholder [48][49][50][51].…”

“…The characteristic business utility includes the impact of a data-driven use case on a process or an enterprise [12,26,61]. In addition, the characteristic cost may include expenses associated with the data being evaluated, such as data collection, processing, analysis, and management costs [29,34,[62][63][64] as well as opportunity costs [35,61,65]. Furthermore, we complement data management related data value drivers and tailor them into the characteristics data durability and lifetime [66, 67], data quality [51,61,64,68,69], and data security and privacy [64, [70][71][72].…”

“…The rst characteristic economic comprises all theories that determine the data value based on price-quantity diagrams, cost curves and conventional hardware-oriented pricing (cost, competition, customer) [29,32,57]. In addition, game theory can be used as a data valuation theory, which can be divided into two characteristics, cooperative [30,31] and non-cooperative game theory [63,75].…”