Toward Personalized Location Privacy Trading for Mobile Crowd Sensing

This research proposes Leaper, a framework that helps mobile workers in crowdsourcing tasks (where many people contribute data from their phones) protect their location privacy while still completing work. The system uses differential privacy (a mathematical technique that adds noise to data to prevent identifying individuals) and k-anonymity (mixing a person's data with others so they can't be singled out) to obfuscate, or hide, each worker's actual location, and then compensates workers fairly based on the privacy risk they accept.