Protecting Location
Privacy in Sensor Networks against a Global Eavesdropper
ABSTRACT:
While many protocols for sensor network
security provide confidentiality for the content of messages, contextual information
usually remains exposed. Such contextual information can be exploited by an
adversary to derive sensitive information such as the locations of monitored
objects and data sinks in the field. Attacks on these components can
significantly undermine any network application. Existing techniques defend the
leakage of location information from a limited adversary who can only observe network
traffic in a small region. However, a stronger adversary, the global
eavesdropper, is realistic and can defeat these existing techniques. This paper
first formalizes the location privacy issues in sensor networks under this
strong adversary model and computes a lower bound on the communication overhead
needed for achieving a given level of location privacy. The paper then proposes
two techniques to provide location privacy to monitored objects
(source-location privacy)—periodic collection and source simulation—and two
techniques to provide location privacy to data sinks (sink-location
privacy)—sink simulation and backbone flooding. These techniques provide
trade-offs between privacy, communication cost, and latency. Through analysis
and simulation, we demonstrate that the proposed techniques are efficient and
effective for source and sink-location privacy in sensor networks.
Architecture:
EXISTING
SYSTEM:
However,
these existing solutions can only be used to deal with adversaries who have
only a local view of network traffic. A highly motivated adversary can easily
eavesdrop on the entire network and defeat all these solutions. For example,
the adversary may decide to deploy his own set of sensor nodes to monitor the
communication in the target network. However, all these existing methods assume
that the adversary is a local eavesdropper. If an adversary has the global
knowledge of the network traffic, it can easily defeat these schemes. For
example, the adversary only needs to identify the sensor node that makes the
first move during the communication with the base station. Intuitively, this
sensor node should be close to the location of adversaries’ interest.
Disadvantages:
However,
these existing approaches assume a weak adversary model where the adversary
sees only local network traffic.
PROPOSED
SYSTEM:
We show
the performance of the proposed privacy-preserving techniques in terms of
energy consumption and latency and compare our methods with the phantom
single-path method, a method that is effective only against local
eavesdroppers. For the purpose of simulation, we assume that the network
application only needs to detect the locations of pandas and always wants to
know the most recent locations. We thus have every sensor node drop a new
packet if it has already queued a packet that was generated on the same event.
In our simulation, we assume that the adversary has deployed a network to
monitor the traffic in the target network.
Advantages:
Specifically,
he is able to locate every sensor node in the target network and eavesdrop
every packet this node delivers.
Modules:
1.
Attackers Modules.
2.
Privacy-Preserving Routing Techniques.
3.
Adversary Model.
4.
Privacy Evaluation Model.
5.
Security Analysis.
1.
Attackers Modules:
The
appearance of an endangered animal (Attackers) in a monitored area is survived
by wireless sensor, at the each time the inside and outside sensors are sensing
to find out the attackers location and the timing. This information is passed
to the server for analyzing. After analyzing the commander and Hunter they are
also can participate this wireless network. In the commander and hunter itself
some intruders are there, our aim to capture the attackers before attempting
the network.
2.
Privacy-Preserving Routing Techniques:
This
section presents two techniques for privacypreserving routing in sensor
networks, a periodic collection method and a source simulation method.
The periodic collection method achieves the optimal location privacy but can
only be applied to applications that collect data at a low rate and do not have
strict requirements on the data delivery latency. The source simulation method
provides practical trade-offs between privacy, communication cost, and latency;
it can be effectively applied to real-time applications. In this paper, we
assume that all communication between sensor nodes in the network is protected
by pair wise keys so that the contents of all data packets appear random to the
Global eavesdropper. This prevents the adversary from correlating different
Data packets to trace the real object.
3.
Adversary Model:
For the
kinds of wireless sensor networks that we envision, we expect highly-motivated
and well-funded attackers whose objective is to learn sensitive location-based
information. This information can include the location of the events detected
by the target sensor network such as the presence of a panda. The Panda- Hunter
example application was introduced in, and we will also use it to help describe
and motivate our techniques. In this application, a sensor network is deployed
to track endangered giant pandas in a bamboo forest. Each panda has an
electronic tag that emits a signal that can be detected by the sensors in the
network. A clever and motivated poacher could use the communication in the
network to help him discover the locations of pandas in the forest more quickly
and easily than by traditional tracking techniques. In any case, it should be
feasible to monitor the communication patterns and locations of events in a
sensor network via global eavesdropping. An attacker with this capability poses
a significant threat to location privacy in these networks, and we therefore
focus our attention to this type of attacker.
4.
Privacy Evaluation Model:
In this
section, we formalize the location privacy issues under the global eavesdropper
model. In this model, the adversary deploys an attacking network to
monitor the sensor activities in the target network. We consider a powerful
adversary who can eavesdrop the communication of every Sensor node in the
target network. Every sensor node i in the target network is an observation
point, which produces an observation (i, t, d) whenever it transmits a
packet d in the target network at time t. In this paper, we assume that the
attacker only monitors the wireless channel and the contents of any data packet
will appear random to him.
5.
Security Analysis:
The generation
number of a packet can be hidden in the secure routing scheme through
link-to-link encryption. In this way, attackers cannot find the generation
number of a packet for their further analysis. Notice that secure routing paths
are only required to be established at the beginning of each session; during
the packet transmission, secure routing paths are not required to change or
re-established for each new generation.
Algorithm:
Localization
algorithm:
where
¨OT is the set of all possible observations, i.e., ¨OT ={(i, t)}i_I,0≤t≤T . This function returns the identity of the location of the object at
time T , if the set of observations is a candidate trace, and returns _ otherwise. For simplicity, we assume that the
pattern analysis does not return fractional values, e.g. a probabilistic
measure of the chance that a trace is a candidate trace or not. We say that a
pattern analysis function is perfect if it can identify all candidate
traces without error, i.e. without false positives or false negatives. In this
paper, we consider a strong adversary who uses a perfect pattern analysis
function.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard
Disk : 40 GB.
•
Floppy
Drive : 1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse : Logitech.
•
Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
•
Operating system : - Windows XP.
•
Coding Language : J2EE
•
Data Base : MYSQL
REFERENCE:
Kiran Mehta, Donggang and Matthew
Wright, “Protecting Location Privacy in Sensor Networks against a Global
Eavesdropper”, IEEE TRANSACTIONS ON
MOBILE COMPUTING, VOL. 11, NO. 2, FEBRUARY 2012.