People spend 80-90% of their time in indoor environments such as offices, undergrounds, shopping malls and airports. On the other hand, the uptake of interesting applications in indoor spaces (e.g., navigation, inventory management and elderly support) has so far been hampered by the lack of technologies that can provide indoor location (position) accurately, in real-time, in an energy-efficient manner and without expensive additional hardware. Modern smartphones currently rely on cloud-based Indoor Positioning Services (IPS), which can provide the location of a user upon request but those are both inaccurate and additionally raise important location privacy concerns, as the IPS can know where the user is at all times. In this talk, I will start out by overviewing the building blocks of Anyplace, our in-house IPS that recently won several international research awards for its accuracy (i.e., less than 2 meters) and utility. Anyplace deploys a number of innovative concepts, including crowdsourcing, big-data management, energy-aware processing, multi-device optimization and mobile data management, in order to realize a power-efficient and accurate indoor localization and navigation technology. In the second part of this talk, I will focus on an algorithm we developed for protecting users from location tracking by the IPS, without hindering the provisioning of fine-grained location updates on a continuous basis. Our algorithm exploits a k-Anonymity Bloom filter and a generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. My talk will be succeeded by a summary of related research efforts, namely SmartLab, which is a novel in-house programming cluster of smartphones that we use in our experimental studies; and Rayzit which is an award-winning location-based crowd messaging service that addresses big-data velocity with parallel algorithms and distributed NoSQL databases.