Dynamic Privacy Pricing For Timely Rewards

Giving patients timely, accurate pricing.

Personalized pricing versus history-based pricing: implications for privacy policy

We compare personalized and history-based pricing and show that personalized pricing harms consumer surplus and total welfare when evaluated over a two-period horizon. The model is characterized by two key features: 1. The discounted two-period profits are invariant to whether personalized or history-based pricing is applied because higher period-2 profits with personalized pricing are offset b...

Privacy Preserving Dynamic Access Control Model with Access Delegation for eHealth

eHealth is the concept of using the stored digital data to achieve clinical, educational, and administrative goals and meet the needs of patients, experts, and medical care providers. Expansion of the utilization of information technology and in particular, the Internet of Things (IoT) in eHealth, raises various challenges, where the most important one is security and access control. In this re...

Privacy-Preserving Electronic Toll System with Dynamic Pricing for Low Emission Zones

Low emission zones (LEZs) aim to reduce pollution and traffic congestion in cities. Current proposals for managing LEZs introduce a significant error percentage in the detection of fraudulent drivers and represent a serious privacy threat for the honest ones. In this article, a new electronic toll system to improve both issues is proposed.

Thompson Sampling for Dynamic Pricing

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various param...