The rapid developments of mobile devices and online social networks have resulted in increasing attention to Mobile Social Networking (MSN). The explosive growth of mobile-connected and location-aware devices makes it possible and meaningful to do the Proximity-based Mobile Social Networks (PMSNs). Users can discover and make new social interactions easily with physical-proximate mobile users through WiFi/Bluetooth interfaces embedded in their smartphones. However, users enjoy these conveniences at the cost of their growing privacy concerns. To address this problem, we propose a suit of priority-aware private matching schemes to privately match the similarity with potential friends in the vicinity. Unlike most existing work, our proposed priority-aware matching scheme (P-match) achieves the privacy goal by combining the commutative encryption function and the Tanimoto similarity coefficient which considers both the number of common attributes between users as well as the corresponding priorities on each common attribute. Further, based on the newly constructed similarity function which takes the ratio of attributes matched over all the input set into consideration, we design an enhanced version to deal with some potential attacks such as unlimitedly inputting the attribute set on either the initiator side or the responder side, etc. Finally, our proposed E-match avoids the heavy cryptographic operations and improves the system performance significantly by employing a novel use of the Bloom filter. The security and communication/computation overhead of our schemes are thoroughly analyzed and evaluated via detailed simulations and implementation.