Towards a Framework for Privacy-Aware Mobile Crowdsourcing

Abstract

The practice of employing "the crowd" to help solve an organization's problems first became popular in the business sector, and has since spread to public and not-for-profit organizations. Input from the crowd can be solicited using different mechanisms involving various types of web-based applications, or the more recent trend of employing mobile phones with sensing capabilities. However, these crowd sourcing systems may lead to various privacy and security risks which can then hinder the adoption of these services. How to identify and address these potential risks in such systems has both research and practical value. This paper presents two aspects of our work in this emerging space. First, we describe a survey of potential privacy and security risks in mobile crowd sourcing systems (MCSS). Second, we describe our PEALS framework to support privacy-aware mobile crowd sourcing.