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An example of proximity discovery. Alice and Bob are friends. Now Alice is shop-
ping in a shopping mall. Her mobile is turned on and so is the bluetooth in the mobile.
Bob has just stepped into that mall, then Alice is being noticed by her mobile that Bob
is in the same hall too. Therefore, they will meet and have a pleasant time together.
Typical proximity discovery. In typical proximity discovery systems, users repeatedly
annotate location tags according to the information given by their handheld devices
(e.g., mobile phone and PDA), send the location tags to the server, and then the server
will find out proximities of each user based on doing comparisons between those tags.
For example, if the server discovers that the location in a location tag M 1 sent by user
U 1 is the same as or similar with the location in another location tag M 2 sent by user U 2 .
Then the server may notice U 1 and U 2 that they are proximities of each other. However,
there may be tag spam in the location tags. Here we define “tag spam” as a location tag
whose content doesn't match the location where the user resides. Obviously, if there are
tag spam, then the server will do wrong comparisons and therefore return wrong results
(i.e., wrong proximities) to the users. For example, Bob sends a location tag, which
indicates that he is shopping however he is at home actually, to the server. One friend of
Bob (say Alice ) also sends a location tag, which indicates that she is shopping which
is true, to the server. The server will tell Alice that Bob is nearby based on comparison
between location tags, however, Alice will find herself be cheated.
Our goal. This paper aims to answer the following questions: Is it possible to design an
approach that helps the users to discover their proximities with fewer mistakes which
are caused by wrong location tags? To answer the question, we aim for effective mecha-
nism that helps users to discover their proximities with high efficiency and fewer wrong
results.
Our approach and contributions. In this paper we present Produre, a novel proximity
discovery mechanism based on users' credibilities in location tagging system and online
social network. The introduction of online social network helps to maintain the rela-
tionship and location exposure policies between friends. The users' credibilities help to
diminish the bad influence of malicious users who always annotate wrong location tags
because the credibility scores change based on users' performance. The experimental
results by our prototype illustrate Produre can effectively discover proximities of a user
in an efficient and accurate way with high quality and fewer mistakes.
To the best of our knowledge, there is not any study, using the method similar with
Produre. The contributions of this paper are as follows:
- We propose Produre, a novel proximity discovery mechanism in location tagging
system based on users' credibilities.
- We introduce online social network to maintain the relationship and location expo-
sure policies between users in Produre.
- We are the first to introduce approach to overcome the proximity discovery problem
in current social-network services.
Outline. The rest of this paper is organized as follows. Section 2 describes the de-
sign rationale of Produe. Then, the evaluation results are discussed in Section 3. We
present the related work in Section 4. Finally, we present conclusion and future work in
Section 5.
 
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