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A Novel Time-Obfuscated Algorithm for
Trajectory Privacy Protection
ABSTRACT:
Location-based services (LBS) which bring so much convenience to our
daily life have been intensively studied over the years. Generally, an LBS
query processing can be categorized into snapshot and continuous queries
which access user location information and return search results to the
users. An LBS has full control of the location information, causing user
privacy concerns. If an LBS provider has a malicious intention to breach
the userprivacy by tracking the users’ routes to their destinations, it incurs
a serious threat. Most existing techniques have addressed privacy
protection mainly for snapshot queries. However, providing privacy
protection for continuous queries is of importance, since a malicious LBS
can easily obtain complete user privacy information by observing a
sequence of successive query requests. In this paper, we propose a
comprehensive trajectory privacy technique and combine ambient
conditions to cloak location information based on the user privacy profile
to avoid a malicious LBS reconstructing a user trajectory. We first propose
an r-anonymity mechanism which pre-processesa setof similar trajectories
R to blur the actual trajectory of a service user. We then combine k
anonymity with s road segments to protect the user’s privacy. We
introduce a novel time-obfuscated technique which breaks the sequence of
the query issuing time for a service user to confuse the LBS so it does not
know the user trajectory, by sending a query randomly from a set of

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#1, Ist
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locations residing at the different trajectories in R. Despite the randomness
incurred from the obfuscation process for providing strong trajectory
privacy protection, the experimental results show that our trajectory
privacy technique maintains the correctness of the query results at a
competitive computational cost.
EXISTING SYSTEM:
We use a comprehensive trajectory privacy technique and combine
ambient conditions to cloak location information based on the user privacy
profile to avoid a malicious LBS reconstructing a user trajectory. We first
propose an r-anonymity mechanism which pre-processes a set of similar
trajectories R to blur the actual trajectory of a service user. We then
combine k-anonymity with s road segments to protect the user’s privacy.
The existing techniques for protecting trajectory privacy have some
weaknesses. The location correlation attacks may be prevented by some
existing approach such as the trajectory k-anonymity paradigm, in which
the k users who participate in the original k-ASR are not allowed to leave
the group. However, a major drawback is that the cloaking region
increases quicklyas these k users move away from each other as time goes
by, and hence, the quality of service is degraded.
PROPOSED SYSTEM:
We have proposed a novel technique for trajectory privacy protection,
which considers a long-term disclosure issue as well as the real-time

3. ECRUITMENTSOLUTIONS (0)9751442511, 9750610101
#1, Ist
Cross, Ist
Main Road, Elango Nagar,Pondicherry-605 011. tech@ecruitments.com
www.ecruitments.com
ambient conditions. We introduce a time-obfuscated technique to
dramatically increase the level of trajectory privacy protection. We allow
the users to specify their own privacy profile, and our time obfuscated
method integrates the r-anonymity, k-anonymity and s-segmentparadigms.
We plan to investigate peer-to-peer architecture for connecting personal
data vaults based on users’ on-line social relationships (e.g., friends in a
socialnetworking site such as Facebook) suchthat a group of personal data
vaults would behave as distributed trusted anonymity servers.
CONCLUSION:
In this paper, we have proposed a novel technique for trajectory privacy
protection, which considers a long-term disclosure issue as well as the real
time ambient conditions. We introduce a time-obfuscated technique to
dramatically increase the level of trajectory privacy protection. We allow
the users to specify their own privacy profile, and our time obfuscated
method integrates the r-anonymity, k-anonymity and s-segmentparadigms.
As the experimental results have shown, our privacy technique is able to
protect the user trajectory and prevent the LBS from reconstructing a
user’s actual trajectory and the direction. Our method guarantees
satisfying the user’s privacy profile for providing stronger privacy
protection.