Privacy in Urban Sensing with Instrumented Fleets, Using Air Pollution Monitoring As A Usecase

Companies providing services like cab sharing, e-commerce logistics and, food delivery are willing to instrument their vehicles for scaling up measurements of traffic congestion, travel time, road surface quality, air quality, etc.~cite{polmeasure}. Analyzing fine-grained sensors data from such large fleets can be highly beneficial; however, this sensor information reveals the locations and the number of vehicles in the deployed fleet. This sensitive data is of high business value to rival companies in the same business domain, e.g., Uber vs. Ola, Uber vs. Lyft in cab sharing, or Amazon vs. Alibaba in the e-commerce domain. This paper provides privacy guarantees for the scenario mentioned above using Gaussian Process Regression (GPR) based interpolation, Differential Privacy (DP), and Secure two-party computations (2PC). The sensed values from instrumented vehicle fleets are made available preserving fleet and client privacy, along with client utility. Our system has efficient latency and bandwidth overheads, even for resource-constrained mobile clients. To demonstrate our end-to-end system, we build a sample Android application that gives the least polluted route alternatives given a source-destination pair in a privacy preserved manner.