A Privacy-Enhanced and Personalized Safe Route Planner with Crowdsourced Data and Computation

We introduce a novel safe route planning problem and develop an efficient solution to ensure the travelers’ safety on roads. Though few research attempts have been made in this regard, all of them assume that people share their sensitive travel experiences with a centralized entity for finding the safest routes, which is not ideal in practice for privacy reasons. Furthermore, existing works formulate the safe route planning query in ways that do not meet a traveler’s need for safe travel on roads. Our approach finds the safest routes within a user-specified distance threshold based on the personalized travel experience of the knowledgeable crowd without involving any centralized computation. We develop a privacy preserving model to quantify the travel experience of a user into personalized safety scores. Our algorithms for finding the safest route further enhance user privacy by minimizing the exposure of personalized safety scores with others. We implement a working prototype of our solution on the Android platform. Extensive experiments using real datasets show that our approach finds the safest route in seconds with 50% less exposure of personalized safety scores.