In recent years, ride-hailing services (RHSs) have be- come increasingly popular, serving millions of users per day. Such systems, however, raise significant privacy concerns, because service providers are able to track the precise mobility patterns of all riders and drivers. In this paper, we propose ORide (Oblivious Ride), a privacy- preserving RHS based on somewhat-homomorphic en- cryption with optimizations such as ciphertext packing and transformed processing. With ORide, a service provider can match riders and drivers without learning their identities or location information. ORide offers rid- ers with fairly large anonymity sets (e.g., several thou- sands), even in sparsely populated areas. In addition, ORide supports key RHS features such as easy payment, reputation scores, accountability, and retrieval of lost items. Using real data-sets that consist of millions of rides, we show that the computational and network over- head introduced by ORide is acceptable. For example, ORide adds only several milliseconds to ride-hailing op- erations, and the extra driving distance for a driver is less than 0.5 km in more than 75% of the cases evaluated. In short, we show that a RHS can offer strong privacy guar- antees to both riders and drivers while maintaining the convenience of its services.