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SDDP: sensitive data detection method for user-controlled data pricing

APPLIED INTELLIGENCE 2025年第6期55卷 1-15页

作者： Hu, Yuchuan Hu, Bitao Guo, Bing Dai, Cheng Shen, Yan Sichuan Univ Coll Comp Sci 24 South Sect 1First Ring Rd Chengdu 610065 Sichuan Peoples R China Chengdu Univ Informat Technol Sch Comp Sci 24 Xuefu Rd Chengdu 610225 Sichuan Peoples R China

In the era of big data, there is an urgent need for data sharing, in which data pricing is a crucial issue, because a reasonable price can not only enhance the willingness of users to share data but also promote the progress of data sharing. However, current research is mostly approached from the perspective of data sharing platforms, treating all data equally without sufficient evaluation of sensitive data within shared datasets and personalized perception of privacy from the users themselves. To address this problem, we detected sensitive data in each piece of data and then defined the pricing function based on information entropy and the user's perception of sensitive information. To enhance the accuracy of sensitive data detection, we integrated an attention mechanism into a pre-trained model to comprehensively represent the samples. Subsequently, on the basis of automatically generating label correlation vectors to calculate the correlation matrix, a graph convolutional neural network was employed to mine the correlation between labels. Furthermore, based on the detection results, information entropy and user ratings are reasonably mapped to prices. pricing based on user ratings is more suitable for pricing personal data rather than government or institutional data. The experimental results on the dataset of Twitter text sent by users have demonstrated that the average precision of our sensitive data detection model has improved by up to 9.26% compared to comparison models, and SDDP can provide reasonable pricing for samples containing sensitive data and fair compensation for users.

关键词： Representation learning Label association data pricing Information entropy

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MAB-RSP: data pricing based on Stackelberg game in MCS

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ARRAY 2025年 26卷

作者： Sun, Yongjiao Ma, Xueyan Han, Anrui Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

With the proliferation of mobile smart devices and wireless communication technologies, Mobile CrowdSensing (MCS) has emerged as a significant data collection method. MCS faces two key challenges: selecting high quality data sellers with unknown reliability and determining fair compensation that addresses device wear and privacy risks. We introduce two novel contributions. First, the MAB-RS algorithm leverages multi-armed bandit reinforcement learning and a data freshness model to dynamically optimize seller recruitment, efficiently balancing exploration of unknown sellers and exploitation of high-quality ones. Second, the MAB-RSP employs a Stackelberg game framework, enabling platforms and sellers to collaboratively maximize profits through strategic pricing and participation incentives. Experiments demonstrate that the algorithm improves revenue while ensuring balanced benefits for all participants.

关键词： data pricing Mobile crowdSensing Stackelberg game Multi-armed bandit

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Developing a data pricing framework for data exchange

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FUTURE BUSINESS JOURNAL 2025年第1期11卷 1-18页

作者： Majumdar, Rupsa Gurtoo, Anjula Maileckal, Minnu Indian Inst Technol Kanpur Dept Econ Sci Kanpur 208016 India Indian Inst Sci IISc Ctr Soc & Policy Bangalore 560012 India

Despite emergence of data markets such as Windows Azure Marketplace and India Urban data Exchange (IUDX), comprehensive frameworks to determine data pricing and/or determine parameters for profit maximization remain a gap. data valuation often gets guided by the sellers, ignoring the interests of the buyers. The information asymmetry results in lopsided pricing. The data sellers fail to price optimally, and the buyers are unable to optimize their purchasing decisions, thus, reinforcing the need for a structured data pricing framework. The paper reviews literature and applies the stages as reported by Ritchie and Spencer (in: Bryman, Burgess (eds) Analysing qualitative data, Routledge, London, 1994) for applied policy research to determine the main approaches of data pricing and develop a comprehensive pricing framework. Literature selection on pricing attributes and content analysis classifies data pricing models into five broad but distinct themes, based on the data pricing method, namely data characteristics-based pricing, quality-based pricing, query-based pricing, privacy-based pricing, and organizational value-based pricing. Application of the Ritchie and Spencer stages identifies eight factors, namely customer need, customer assigned value, market maturity, market structure, usable data, data quality, seller reputation and seller objectives as defining and intersecting with the five pricing models. A framework is hence developed to guide data pricing. Thereby, the paper creates a platform for prescribing data pricing formulas.

关键词： data pricing framework data attributes data exchange/trading data pricing

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Research on data Resource pricing Method Based on SSA-XGBoost Model

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Journal of Systems Science and Information 2025年第1期13卷 116-136页

作者： Jian YANG Yajuan CHEN Liwei CHANG Yali LÜ School of Information Shanxi University of Finance and EconomicsTaiyuan 030006China

data pricing is a key link to promote the efficient circulation of data in the ***,the existing methods are still insufficient in terms of pertinence,dynamism and ***,we proposed a data pricing prediction model based on sparrow search optimization XGBoost,aiming to provide a reference for pricing decisions in data ***,we crawled the data transaction information of *** and performed preprocessing operations such as outlier processing,one hot encoding and logarithmic transformation on the dataset;Secondly,we conducted exploratory data analysis to understand the distribution of data and their ***,we used the LASSO algorithm to select features for the dataset and constructed a data pricing prediction model based on ***,we compared and analyzed it with six machine learning models including LightGBM,GBDT,MLP,KNN,LR and *** experimental results show that in terms of the R-squared,the prediction results of the proposed SSA-XGBoost model exceed the above six models by 4.9%,7.4%,7.1%,23.8%,12.8%,and 2.3%respectively,and are superior to the state-of-the-art ***,the evaluation results of the five indicators of MSE,RMSE,MAE,MAPE,and RMSPE are better than other models,showing higher stability.

关键词： data pricing lasso SSA-XGBoost machine learning

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data pricing strategy based on data quality

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 112卷 1-10页

作者： Yu, Haifei Zhang, Mengxiao Northeastern Univ Sch Business Adm Shenyang 110819 Liaoning Peoples R China

This paper presents a bi-level mathematical programming model for the data-pricing problem that considers both data quality and data versioning strategies. data products and data-related services differ from information products or services in terms of quality assessment methods. For this problem, we consider two aspects of data quality: (1) its multidimensionality and (2) the interaction between the dimensions. We designed a multi-version data strategy and propose a data-pricing bi-level programming model based on the data quality to maximize the profit by the owner of the data platform and the utility to consumers. A genetic algorithm was used to solve the model. The numerical solutions for the data-pricing model indicate that the multi-version strategy achieves a better market segmentation and is more profitable and feasible when the multiple dimensions of data quality are considered. These results also provide managerial guidance on data provision and data pricing for platform owners. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Big data data marketplace data pricing Production management Bi-level programming model

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data pricing in machine learning pipelines

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第6期64卷 1417-1455页

作者： Cong, Zicun Luo, Xuan Pei, Jian Zhu, Feida Zhang, Yong Simon Fraser Univ Sch Comp Sci Burnaby BC Canada Simon Fraser Univ Comp Sci Dept Burnaby BC Canada Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore Huawei Technol Canada Burnaby BC Canada

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.

关键词： data assets data pricing data products Machine learning AI

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When Users Interfere with Protocols: Prospect Theory in Wireless Networks using Random Access and data pricing as an Example

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2014年第4期13卷 1888-1907页

作者： Li, Tianming Mandayam, Narayan B. Rutgers State Univ ECE Dept WINLAB New Brunswick NJ 08902 USA

Game theoretic models have found widespread use in the analysis and engineered system design of radio resource management algorithms for a wide variety of systems such as cellular, ad hoc and sensor networks. The fundamental principle behind such models and much of game theory has been the reliance on Expected Utility Theory (EUT). Motivated by the increasing amount of end-user control afforded in programmable radio devices, we envision a scenario where end-user actions essentially "interfere" with the underlying engineered system design. As an exemplary scenario, we consider in this paper wireless random access where players follow the precepts of Prospect Theory (PT), a theory developed by Kahneman and Tversky to explain real-life decision making that often deviates from the behavior expected under EUT. Specifically, we consider a game where selfish players adjust their transmission probabilities over a random access channel under throughput rewards, delay penalties and energy costs. By analyzing the Nash Equilibrium achieved, we prove under mild conditions that deviations from EUT results in degradation of system throughput while increasing delay and energy consumption. Finally, we consider a data pricing model and study the impact of end-user decision-making (i.e., players service choices) at the NE on wireless network performance.

关键词： Prospect theory game theory data pricing wireless networks random access

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Block-CPS: Blockchain and Non-Cooperative Game-Based data pricing Scheme for Car Sharing

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IEEE INTERNET OF THINGS JOURNAL 2022年第24期9卷 25780-25790页

作者： Kakkar, Riya Gupta, Rajesh Dahman Alshehri, Mohammad Tanwar, Sudeep Dua, Amit Kumar, Neeraj Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 India Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia Birla Inst Technol & Sci Pilani Dept Comp Sci & Informat Syst Pilani 333031 India Deemed be Univ Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala India Univ Petr Sch Comp Energy Studies Dehra Dun 248007 India

This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems.

关键词： Blockchain car sharing data pricing ethereum non-cooperative game smart contract

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A Survey of data pricing for data Marketplaces

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IEEE TRANSACTIONS ON BIG data 2023年第4期9卷 1038-1056页

作者： Zhang, Mengxiao Beltran, Fernando Liu, Jiamou Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Sichuan Peoples R China Univ Auckland Business Sch Auckland 1142 New Zealand Univ Auckland Sch Comp Sci Auckland 1142 New Zealand

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This article attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.

关键词： data marketplaces data pricing taxonomy of pricing methods

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Distributed Feature Selection Considering data pricing Based on Edge Computing in Electricity Spot Markets

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IEEE INTERNET OF THINGS JOURNAL 2023年第3期10卷 2231-2244页

作者： Hu, Yufei Guan, Xin Hu, Benran Liu, Yongnan Chen, Hongyang Ohtsuki, Tomoaki Heilongjiang Univ Sch Data Sci & Technol Harbin 150080 Peoples R China State Grid Heilongjiang Elect Power Co Ltd Power Trading Ctr Harbin 150080 Peoples R China Res Ctr Intelligent Network Zhejiang Lab Hangzhou 311121 Peoples R China Keio Univ Dept Informat & Comp Sci Yokohama 2238522 Japan

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

关键词： pricing Feature extraction Blockchains Computational modeling Edge computing Task analysis Heuristic algorithms data pricing deep learning Internet of Energy

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