A Survey on Big Data Market: Pricing, Trading and Protection
By Fan Liang, Wei Yu, Dou An, Qingyu Yang, Xinwen Fu, Wei Zhao. IEEE Access, 2018
309 citations recorded by the source corpus.
Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/f673051978825fc796b2b8812808ca6cd8de83f2
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Citations in the corpus
- A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “I. INTRODUCTION Along with Big Data and Analytics [79], [149], Cloud/Edge Computing-based Big Computing [120], [155], and the Internet of Things (IoT)/Cyber-Physical Systems (CPS) [80], [82], [125], [127], [143], [147], [148], [161], the topic of Deep Learning has come to dominate industry and resea”
- A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, methodology): “Enabling big data sharing is important and the key techniques include the design trading platforms and algorithms [98], [99].”