Dynamic Privacy-Aware Collaborative Schemes for Average Computation: A Multi-Time Reporting Case

Wang, Xin; Ishii, Hideaki; He, Jianping; Cheng, Peng

doi:10.1109/tifs.2021.3096121

Cited by 13 publications

(2 citation statements)

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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%

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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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Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%