Recently, mobile social networks (MSNs) have been widely discussed due to the rapid growth of smart mobile devices. This work focuses on mobile D2D social networks (MDSNs), where users in an MSN are physical neighbors. An important social application of MDSNs is common profile matching (CPM), which refers to the scenario where a group of smartphone users meet in a small region (such as a ball room) and these users are interested in identifying the common attributes among them from their

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... profiles efficiently via short-range (such as D2D) communications. For example, a group of strangers may want to find common hobbies, friends, or countries they visited before, and a group of students may want to know the common courses they have ever taken. Assuming that users in an MDSN form a fully connected network, we formulate three versions, namely allcommon, β-common, and top-γ-popular, of the CPM problem. The first problem is an extension of an earlier work, while the latter two problems are newly defined. We present solutions based on the basic and the iterative Bloom filters. Evaluation results show that our mechanisms are quite communication-efficient. Index Terms-Bloom filter, D2D communication, mobile social networks, profile matching, smartphone apps.