Clinical natural language processing (NLP) has become indispensable in the secondary use of electronic medical records (EMRs). However, it is found that current clinical NLP tools face the problem of portability among different institutes. An ideal solution to this problem is cross-institutional data sharing. However, the legal enforcement of no revelation of protected health information (PHI) obstructs this practice even with the availability of state-of-the-art de-identification tools. In this paper, we investigated the use of a frequency-filtering approach to extract PHI-free sentences utilizing the Enterprise Data Trust (EDT), a large collection of EMRs at Mayo Clinic. Our approach is based on the assumption that sentences appearing frequently tend to contain no PHI. This assumption originates from the observation that there exist a large number of redundant descriptions of similar patient conditions in EDT. Both manual and automatic evaluations on the sentence set with frequencies higher than one show no PHI are found. The promising results demonstrate the potential of sharing highly frequent sentences among institutes.