Project Login
Registration No:

Protecting Location Privacy in Sensor Networks Against a Global Eavesdropper


IEEE Projects Years : 2012

Protecting Location Privacy in Sensor Networks Against a Global Eavesdropper

While many protocols for sensor network security provide
confidentiality for the content of messages, contextual information usually remains
exposed. Such information can be critical to the mission of the sensor network,
such as the location of a target object in a monitoring application, and it is often
Important to protect this information as well as message content. There have been
several recent studies on providing location privacy in sensor networks. We first
argue that a strong adversary model, the global eavesdropper, is often realistic in
practice and can defeat existing techniques. We then formalize the location privacy
issues under this strong adversary model and show how much communication
overhead is needed for achieving a given level of privacy. We also propose two
techniques that prevent the leakage of location information: periodic collection and
source simulation. Periodic collection provides a high level of location privacy,
while source simulation provides trade-offs between privacy, communication cost,
and latency. Through analysis and simulation, we demonstrate that the proposed
techniques are efficient and effective in protecting location information from the
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.
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.
Specifically, he is able to locate every sensor node in the target
network and eavesdrop every packet this node delivers.
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.
Localization algorithm:
where ¨OT is the set of all possible observations, i.e., ¨OT
={(i, t)}iI,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 :
• Data Base : SQL Server 2005






CALL: 08985129129 ,  E-Mail Id: