Mobile applications (apps), though useful, can create privacy risks for their users by leaking sensitive information from SMS text messages, location data, contacts, and photos, etc. to the app developers or to data brokers. This is a critical problem since both the number of mobile applications and people's use of them have grown greatly over the last decade. This expansion of choices and uses makes it hard for people to find apps that meet both their functional and privacy needs. Recommender systems, which provide personalized suggestions based on user ratings, usage, and other data, are often used in other domains to help people decide between many choices. However, applying existing recommendation methods to mobile apps can expose users to options that may appear useful but pose substantial privacy risks. This project introduces a new genre of recommender systems that selects a small set of candidate apps that might work for people based on their functional needs; analyzes the way those apps access, transform, and share data; and combines those rankings with people's privacy expectations to suggest apps that best meet individual people's needs. Further, the system will be designed to clearly communicate the privacy risks involved with suggested apps. Together, the work will promote safer, more trustworthy mobile experiences while advancing people's understanding of privacy online. The project will achieve this goal by grounding conflicting information about app beh