Twitter is one of the most popular Online Social Networks (OSNs) nowadays.  Twitter users retrieve information from other users by subscribing to their tweets.  Twitter users, especially those who have many followees, may receive hundreds or even thousands of tweets daily.  Currently, all tweets are shown to users in chronological order.  Consequently, a Twitter user may accidentally overlook useful and interesting tweets because the user is overwhelmed by the huge volume of uninteresting tweets.  Researchers in the recommendation system community have proposed using recommendation techniques such as collaborative filtering to predict users' preference of tweets and highlight those tweets in which users are most likely to be interested.  At the same time, while OSNs such as Twitter have enabled people to conveniently share information and interact with each other online, OSN users are getting increasingly concerned about their online privacy.  Researchers in the security community have proposed using techniques such as encrypted tweets to protect users' privacy.  In this paper, we propose a privacy-preserving personalized tweet recommendation framework, pTwitterRec, in a Twitter-like social network where users' tweets are hidden from the OSN provider.  pTwitterRec provides users with personalized tweet recommendations while keeping users' tweets and interests hidden from the OSN provider as well as other unauthorized entities.  pTwitterRec splits the tweet recommendation task between the provider and a semi-trusted third party, so that neither can derive users' sensitive information alone while working together to provide users with personalized tweet recommendations.  We implement a prototype and demonstrate through evaluation that pTwitterRec incurs tolerable overhead on today's smartphones.