  Identifying user's identity is a key problem in many data mining
applications, such as product recommendation, customized content delivery and
criminal identification. Given a set of accounts from the same or different
social network platforms, user identification attempts to identify all accounts
belonging to the same person. A commonly used solution is to build the
relationship among different accounts by exploring their collective patterns,
e.g., user profile, writing style, similar comments. However, this kind of
method doesn't work well in many practical scenarios, since the information
posted explicitly by users may be false due to various reasons. In this paper,
we re-inspect the user identification problem from a novel perspective, i.e.,
identifying user's identity by matching his/her cameras. The underlying
assumption is that multiple accounts belonging to the same person contain the
same or similar camera fingerprint information. The proposed framework, called
User Camera Identification (UCI), is based on camera fingerprints, which takes
fully into account the problems of multiple cameras and reposting behaviors.
