# Privacy Management and Optimal Pricing in People-Centric Sensing

## TL;DR

This paper analyzes privacy-privacy trade-offs and optimal pricing strategies for people-centric services, proposing profit maximization models and examining the effects of service bundling on profitability.

## Contribution

It introduces closed-form solutions for optimal privacy and pricing, and evaluates the profitability of bundling complementary versus substitute services.

## Key findings

- Cooperation via bundling is profitable for complementary services.
- Bundling is detrimental for substitute services.
- Market value of bundles depends on inter-service contingency.

## Abstract

With the emerging sensing technologies such as mobile crowdsensing and Internet of Things (IoT), people-centric data can be efficiently collected and used for analytics and optimization purposes. This data is typically required to develop and render people-centric services. In this paper, we address the privacy implication, optimal pricing, and bundling of people-centric services. We first define the inverse correlation between the service quality and privacy level from data analytics perspectives. We then present the profit maximization models of selling standalone, complementary, and substitute services. Specifically, the closed-form solutions of the optimal privacy level and subscription fee are derived to maximize the gross profit of service providers. For interrelated people-centric services, we show that cooperation by service bundling of complementary services is profitable compared to the separate sales but detrimental for substitutes. We also show that the market value of a service bundle is correlated with the degree of contingency between the interrelated services. Finally, we incorporate the profit sharing models from game theory for dividing the bundling profit among the cooperative service providers.