Dynamic Privacy Pricing Based on Reinforcement Learning

Chen, Xi; Yu, Yang; Liang, Ximei; Lei, Min; Bi, Wei

doi:10.1145/3220199.3220227

Cited by 5 publications

(1 citation statement)

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“…Therefore, machine learning and reinforcement learning are powerful assistant tools for addressing those issues. [102] and [103] use reinforcement learning to find out the optimal flat fee and the optimal contract, respectively. Machine learning and reinforcement learning have the potential to be applied to other pricing methods.…”

Section: Future Opportunitiesmentioning

confidence: 99%

A Survey of Data Pricing for Data Marketplaces

Zhang¹,

Beltrán²,

Liu³

2023

Preprint

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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 paper 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.

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Section: Future Opportunitiesmentioning

confidence: 99%