Personal big data pricing method based on differential privacy

Shen, Yuning; Guo, Bing; Shen, Yan; Duan, Xuliang; Dong, Xiangqian; Zhang, Hong; Zhang, Chuanwu; Yu-ming, Jiang

doi:10.1016/j.cose.2021.102529

Cited by 15 publications

(4 citation statements)

References 34 publications

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“…In addition, Shen et al [22] presented a pricing method for personal big data based on differential privacy. They devised both forward pricing and reverse pricing approaches.…”

Section: Privacy-based Pricing Modelsmentioning

confidence: 99%

A Survey on Data Pricing: Methods, Challenges, and Prospects

Chi,

Zhao,

Fang

et al. 2023

Frontiers in Artificial Intelligence and Applications

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As organizations increasingly rely on data-driven decision-making, understanding the true value and potential of data becomes crucial. Data pricing, which aims to determine the financial value of information, plays a pivotal role in data circulation and transactions. However, the existing surveys on data pricing have limitations that need to be addressed. This paper presents a comprehensive survey of big data pricing methods from a data science perspective. It begins by providing an overview of the fundamental concepts underlying data pricing and the data market. Subsequently, it delves into the general principles and challenges associated with data pricing. The survey categorizes and summarizes various approaches and methods employed in data pricing, assessing their respective advantages and disadvantages. In conclusion, this paper identifies potential research directions to enhance our understanding of data pricing. By rectifying the deficiencies in existing surveys, this comprehensive study aims to contribute to the development of effective data pricing strategies and foster advancements in the field of data science.

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“…In addition, Shen et al [22] presented a pricing method for personal big data based on differential privacy. They devised both forward pricing and reverse pricing approaches.…”

Section: Privacy-based Pricing Modelsmentioning

confidence: 99%

A Survey on Data Pricing: Methods, Challenges, and Prospects

Chi,

Zhao,

Fang

et al. 2023

Frontiers in Artificial Intelligence and Applications

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“…All these applications listed in Figure 30 mostly work with realtime data. Recently, Shen et al [318] discussed a real-time pricing method for big data environments based on DP. The proposed method produces aggregated query answers with minimal noise to facilitate data owners and data buyers in a privacy-preserved way.…”

Section: B Potential Opportunities For Future Research In Privacy Domainmentioning

confidence: 99%

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

2022

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With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user's privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

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“…In recent years, there has been increasing focus on compensating the privacy loss of data sellers in data pricing models 4 . Shen et al 5 proposed a balanced pricing mechanism that computes the price of a noisy aggregated query answer and compensates the data owner according to the degree of privacy loss. The work 6 presents a query‐based trading mechanism for data market, where data consumers purchase a targeted aggregation query under a certain perturbation and data owners are compensated for the actual privacy loss brought by the query.…”

Section: Introductionmentioning

confidence: 99%

Data pricing with privacy loss compensation for cyber‐physical systems: A Stackelberg game based approach

Yang

Feng

Wang

et al. 2023

Internet Technology Letters

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The integration of sensors in cyber‐physical systems has given rise to data markets, where data owners can offer their sensing data for sale to potential buyer. However, determining the optimal data price in such markets is a complex issue, which demands a careful consideration of the interests of all parties involved, as well as the potential privacy loss for data sellers. By taking privacy loss into account, this paper proposes a fair compensation model for data sellers and formulates the pricing problem as a Stackelberg game. An automatic data pricing algorithm is developed to calculate the optimal price maximizing the joint benefits of the data sellers and the buyer where the privacy loss of the data sellers are compensated reasonably. Numerical simulations validate the effectiveness of the proposed pricing model in balancing benefits and privacy loss.

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Personal big data pricing method based on differential privacy

Cited by 15 publications

References 34 publications

A Survey on Data Pricing: Methods, Challenges, and Prospects

A Survey on Data Pricing: Methods, Challenges, and Prospects

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

Data pricing with privacy loss compensation for cyber‐physical systems: A Stackelberg game based approach

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