We address issues related to privacy protection in location-based services (LBS). Most existing research in this field either requires a trusted third-party (anonymizer) or uses oblivious protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted thirdparty while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user’s location. Second, an adversary may violate a user’s location privacy in two ways: (i) based on the user’s location information contained in the LBS query payload, and (ii) by inferring a user’s geographical location based on its device’s IP address. To address these challenges, we introduce CAP, a Context-Aware Privacy-preserving LBS system with integrated protect...