Towards personalized privacy preference aware data trading: A contract theory based approach

Feng, Zhenni; Yu, Sijia; Zhu, Yanmin

doi:10.1016/j.comnet.2023.109637

Cited by 8 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In anonymity-based approaches, less accurate data are released through generalization and steganography techniques, thus reducing privacy leakage [19], [20]. In addition, due to the differences in data owners' individualized requirements for privacy protection, some scholars have also proposed a personalized privacy-aware data trading approach based on contract theory to satisfy budget feasibility, incentive compatibility, and individual rationality requirements [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the on-Exchange Implementation of Data Trading Based on Evolutionary Game Theory

Zhang,

Zhang

2024

IEEE Access

View full text Add to dashboard Cite

Floor trading plays a pivotal role in the data trading market. Finding effective solutions to enhance the market share of floor trading is a pressing practical challenge. In this paper, data providers are innovatively taken as the main stakeholders, and parameters such as tax preference ratio and reward threshold are introduced to build a tripartite evolutionary game model of data providers, data trading platforms and government regulatory authorities, explore how participants make strategy choices in the process of increasing exchange trading shares, and analyze and verify the conclusions based on numerical simulation. The research results demonstrate that the evolutionary stabilization strategy is affected by multiple factors, including incentive coefficients, subsidy coefficients, penalty amounts, reward thresholds, incentive subsidy costs, value-added costs and tax incentives ratios. The government's appropriate adjustments to penalties, reward thresholds, and subsidy coefficients can facilitate the participation of suppliers in floor trading and influence the platform's choice of incentive strategy, but increasing the subsidy coefficients is not conducive to the fulfillment of the government's responsibility. Strengthened government supervision regarding the control of platform's value-added costs, higher tax incentives ratio, higher incentive coefficients and costs of incentive subsidy costs for platforms can all help to enhance the likelihood of suppliers' engagement in onplatform transactions. Based on the analysis of the effects of various factors, it provides policy suggestions for the government and platform enterprises to coordinate the regulation of the market, in order to increase the market share.INDEX TERMS Data trading, on-platform implementation, platform incentives, tripartite evolutionary game theory, simulation analysis.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Research on the on-Exchange Implementation of Data Trading Based on Evolutionary Game Theory

Zhang,

Zhang

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Chen et al [36] propose a contract-based edge-assisted federated learning model-sharing incentive mechanism, which maximize the EFL model consumers' proft and ensure the quality of training services. Feng et al [37] incorporate local diferential privacy into contract theory-based private data trading to support personalized privacy preferences. However, most of these incentive mechanisms consider worker contribution based on the data quantity without focusing on data updates in FL.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Incentive Mechanism for Federated Learning: A Single Contract to Dual Contract Approach for Smart Industries

Wan,

Jiang,

Liao

et al. 2024

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Federated learning (FL) has shown promise in smart industries as a means of training machine-learning models while preserving privacy. However, it contradicts FL’s low communication latency requirement to rely on the cloud to transmit information with data owners in model training tasks. Furthermore, data owners may not be willing to contribute their resources for free. To address this, we propose a single contract to dual contract approach to incentivize both model owners and workers to participate in FL-based machine learning tasks. The single-contract incentivizes model owners to contribute their model parameters, and the dual contract incentivizes workers to use their latest data to participate in the training task. The latest data draw out the trade-off between data quantity and data update frequency. Performance evaluation shows that our dual contract satisfies different preferences for data quantity and update frequency, and validates that the proposed incentive mechanism is incentive compatible and flexible.

show abstract

“…Some scholars usually compensates the privacy of data providers by quantifying their privacy exposure [ [50] , [51] , [52] ]. Some academics create a framework for data transactions based on safeguarding the privacy of data sources using cutting-edge computer techniques like smart contracts and GAN generators [ 53 , 54 ]. Query-based pricing is also considered from a privacy perspective [ 55 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The evolution of data pricing: From economics to computational intelligence

Hao,

Deng,

2023

Heliyon

View full text Add to dashboard Cite

Towards personalized privacy preference aware data trading: A contract theory based approach

Cited by 8 publications

References 42 publications

Research on the on-Exchange Implementation of Data Trading Based on Evolutionary Game Theory

Research on the on-Exchange Implementation of Data Trading Based on Evolutionary Game Theory

Hierarchical Incentive Mechanism for Federated Learning: A Single Contract to Dual Contract Approach for Smart Industries

The evolution of data pricing: From economics to computational intelligence

Contact Info

Product

Resources

About