  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.
