Efficient Privacy-Preserving Ridesharing: An Online Matching-Based Approach

Ridesharing apps need to protect user location privacy, but adding random noise to locations (Laplace noise, a mathematical technique that obscures exact positions) makes it harder to match drivers with passengers efficiently. This paper proposes using linear programming (a mathematical optimization method for finding the best solution among many options) to solve the real-time matching problem between ridesharing requests and drivers while maintaining both privacy and matching quality.