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A Theory of Pricing Private Data

Communications of the ACM

When the analysis of individuals' personal information has value to an institution, but it compromises privacy, should individuals be compensated? We describe the foundations of a market in which those seeking access to data must pay for it and individuals are compensated for the loss of privacy they may suffer. The interests of individuals and institutions with respect to personal data are often at odds. Personal data has great value to institutions: they eagerly collect it and monetize it by using it to model customer behavior, personalize services, target advertisements, or by selling the data directly. Yet the inappropriate disclosure of personal data poses a risk to individuals.


Differential Privacy in Personalized Pricing with Nonparametric Demand Models

arXiv.org Machine Learning

In the recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing. However, this leads to the growing concern of potential violation of privacy due to adversarial attack. To address the privacy issue, this paper studies a dynamic personalized pricing problem with \textit{unknown} nonparametric demand models under data privacy protection. Two concepts of data privacy, which have been widely applied in practices, are introduced: \textit{central differential privacy (CDP)} and \textit{local differential privacy (LDP)}, which is proved to be stronger than CDP in many cases. We develop two algorithms which make pricing decisions and learn the unknown demand on the fly, while satisfying the CDP and LDP gurantees respectively. In particular, for the algorithm with CDP guarantee, the regret is proved to be at most $\tilde O(T^{(d+2)/(d+4)}+\varepsilon^{-1}T^{d/(d+4)})$. Here, the parameter $T$ denotes the length of the time horizon, $d$ is the dimension of the personalized information vector, and the key parameter $\varepsilon>0$ measures the strength of privacy (smaller $\varepsilon$ indicates a stronger privacy protection). On the other hand, for the algorithm with LDP guarantee, its regret is proved to be at most $\tilde O(\varepsilon^{-2/(d+2)}T^{(d+1)/(d+2)})$, which is near-optimal as we prove a lower bound of $\Omega(\varepsilon^{-2/(d+2)}T^{(d+1)/(d+2)})$ for any algorithm with LDP guarantee.


Dynamic Pricing in E-Commerce – Mehul Ved – Medium

#artificialintelligence

Dynamic pricing or price optimization is the concept of offering goods at different prices which varies according to the customer's demand. The pricing of the commodity can be done on the basis of competitor's pricing, supply, demand and conversion rates and sales goals. The practice of Dynamic Pricing is being widely adopted in E-Commerce. Machine learning algorithms should be able to efficiently automate pricing decisions to maximize profits, as they can perform pricing decisions using sophisticated calculations and predictions, by putting all available data into perspective, and change their pricing strategy to best adapt to a dynamic environment. Dynamic pricing, a strategy which enables businesses to provide flexible prices for products and services is now catching on across hospitality, retail, travel and entertainment industry segments.


Dynamic ticket pricing taking root in Japan amid pandemic

The Japan Times

Amusement parks, baseball clubs and other entertainment businesses in Japan are increasingly adopting dynamic ticket pricing in a bid to avoid creating crowds amid the COVID-19 pandemic while stabilizing revenue. Those businesses hope that dynamic pricing will help bring in more customers as tickets are cheap on days with low demand. The ticket sales market in Japan in the year ended in February 2021 shrank to a quarter of that of before the pandemic, according to Pia Research Institute, an arm of ticketing agency Pia Corp. Meanwhile, the total value of dynamically priced tickets sold in the country is expected to grow by 1.5-fold to around ¥6.2 billion in the year ending this month from the previous year, according to Dynamic Plus Co., a Mitsui & Co. unit that uses artificial intelligence to offer dynamic pricing services. Under a dynamic pricing plan, prices are changed depending on demand until the day of the event.


Did you pay too much? Samsung phone pricing explained

USATODAY - Tech Top Stories

All phone pricing may not be equal. Here's why you may be paying too much or too little for your new Samsung Galaxy phone. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. All phone pricing may not be equal.