Abstract—Users can enjoy personalized services provided by
various context-aware applications that collect users’ contexts
through sensor-equipped smartphones. Meanwhile, serious pri-
vacy concerns arise due to the lack of privacy preservation
mechanisms. Currently, most mechanisms apply passive defense
policies in which the released contexts from a privacy preserva-
tion system are always real, leading to a great probability with
which an adversary infers the hidden sensitive contexts about
the users. In this paper, we apply a deception policy for pri-
vacy preservation and present a novel technique, FAKEMASK, in
which fake contexts may be released to provably preserve users’
privacy. The output sequence of contexts by FAKEMASK can be
accessed by the untrusted context-aware applications or be used
to answer queries from those applications. Since the output con-
texts may be different from the original contexts, an adversary
has greater difficulty in inferring the real contexts. Therefore,
FAKEMASK limits what adversaries can learn from the output
sequence of contexts about the user being in sensitive contexts,
even if the adversaries are powerful enough to have the knowl-
edge about the system and the temporal correlations among the
contexts. The essence of FAKEMASK is a privacy checking algo-
rithm which decides whether to release a fake context for the
current context of the user. We present a novel privacy checking
algorithm and an efficient one to accelerate the privacy checking
process. Extensive evaluation experiments on real smartphone
context traces of users demonstrate the improved performance
of FAKEMASK over other approaches.
Index Terms—Privacy protection, semi-Markov model,
service.
Abstract—Users can enjoy personalized services provided byvarious context-aware applications that collect users’ contextsthrough sensor-equipped smartphones. Meanwhile, serious pri-vacy concerns arise due to the lack of privacy preservationmechanisms. Currently, most mechanisms apply passive defensepolicies in which the released contexts from a privacy preserva-tion system are always real, leading to a great probability withwhich an adversary infers the hidden sensitive contexts aboutthe users. In this paper, we apply a deception policy for pri-vacy preservation and present a novel technique, FAKEMASK, inwhich fake contexts may be released to provably preserve users’privacy. The output sequence of contexts by FAKEMASK can beaccessed by the untrusted context-aware applications or be usedto answer queries from those applications. Since the output con-texts may be different from the original contexts, an adversaryhas greater difficulty in inferring the real contexts. Therefore,FAKEMASK limits what adversaries can learn from the outputsequence of contexts about the user being in sensitive contexts,even if the adversaries are powerful enough to have the knowl-edge about the system and the temporal correlations among thecontexts. The essence of FAKEMASK is a privacy checking algo-rithm which decides whether to release a fake context for thecurrent context of the user. We present a novel privacy checkingalgorithm and an efficient one to accelerate the privacy checkingprocess. Extensive evaluation experiments on real smartphonecontext traces of users demonstrate the improved performanceof FAKEMASK over other approaches.Index Terms—Privacy protection, semi-Markov model,service.
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