@article{Tang2026, 
author = {Mingjian Tang and Weiqi Wang and Shui Yu},
title = {Privacy Pricing: A Survey from the Perspective of Privacy Business},
year = {2026},
journal = {Big Data Mining and Analytics},
volume = {9},
number = {6},
pages = {1464-1492},
keywords = {privacy pricing, privacy valuation, privacy security},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020096},
doi = {10.26599/BDMA.2025.9020096},
abstract = {Privacy is valuable, especially in the current digital economy with its thriving online services. The value and price of the private data perform a critical role in most of economic activities like trading and sharing, and even in the security protection process. The price also embodies the value of privacy itself, which raises people’s awareness of their privacy. Existing work on pricing varies with different purposes and methods, and accordingly the term “price of privacy” has not found a consistent and consensus definition, which poses difficulties for the future research in this domain. To address this and offer a clear map for the work in pricing private data, we summarise the existing work on privacy pricing into two major business on the market including trading and protection, and further classify them in terms of different pricing methods. We also evaluate the contribution for each pricing model, and we find that there is work to do to improve the pricing model themselves, and address privacy security problems with privacy pricing, especially in the new era of generative artificial intelligence.}
}