Based on spatial and temporal big data, location- based services (LBSs) obtain considerable attention due to their potential to make cities smarter. However, LBS provides the functionality by extracting personal data from users, which may incur privacy leakage risk, such as intrusion, theft, and unauthorized sale of sensitive information. Hence, the mass market share of LBS depends on how well the privacy of citizens can be protected. In this article, we specifically study the privacy issues in LBSs. In detail, we start with classifying different types of LBSs and analyzing the privacy leakage presented in each type. After summarizing the state-of-the-art work on location privacy and query privacy, we propose a query content preservation approach with the aim of providing accurate LBS answer with zero server knowledge on query content. Finally, we present some open problems that may foster future research on LBS privacy preservation.