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