  Social sensing is a paradigm that allows crowdsourcing data from humans and
devices. This sensed data (e.g. social network posts) can be hosted in
social-sensor clouds (i.e. social networks) and delivered as social-sensor
cloud services (SocSen services). These services can be identified by their
providers' social network accounts. Attackers intrude social-sensor clouds by
cloning SocSen service providers' user profiles to deceive social-sensor cloud
users. We propose a novel unsupervised SocSen service provider identity cloning
detection approach, NPS-AntiClone, to prevent the detrimental outcomes caused
by such identity deception. This approach leverages non-privacy-sensitive user
profile data gathered from social networks to perform cloned identity
detection. It consists of three main components: 1) a multi-view account
representation model, 2) an embedding learning model and 3) a prediction model.
The multi-view account representation model forms three different views for a
given identity, namely a post view, a network view and a profile attribute
view. The embedding learning model learns a single embedding from the generated
multi-view representation using Weighted Generalized Canonical Correlation
Analysis. Finally, NPS-AntiClone calculates the cosine similarity between two
accounts' embedding to predict whether these two accounts contain a cloned
account and its victim. We evaluated our proposed approach using a real-world
dataset. The results showed that NPS-AntiClone significantly outperforms the
existing state-of-the-art identity cloning detection techniques and machine
learning approaches.
