50120140505014

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 5, Issue 5, May (2014), pp. 120-128 © IAEME
120
METHODOLOGY FOR DIFFERENT SOURCE LOCATION PRIVACY
PRESERVATION USING IMPROVED STATISTICAL FRAMEWORK IN
WIRELESS SENSOR NETWORKS
Sonali Pawar
Department of Computer Engineering, BSIOTR Wagholi, Pune University, Pune
Prof. Mrs. R. A. Mahajan
Department of Computer Engineering, BSIOTR Wagholi, Pune University, Pune
I. ABSTRACT
Many attackers use the nodes location information for security threat in network, as every
sensor node in network is having its own location. An attacker tries to get location information of
source or receiver. Wireless sensor networks (WSNs) are group of collection of sensor nodes which
are collaboratively communicate with each other for information sharing. The sharing of information
is done between source sensor node and sink sensor node using the routing protocols. The major
constraint of this network is the security. Thus we need to have source location privacy methods in
WSN to prevent such threats from the network. Source privacy preservation is also called source
anonymity and recent time this attracts many researchers interests. The attacker or unauthorized
sensor node not able to get events location information through the current network traffic is called
as source anonymity problem. Recently we have studied efficient method for source anonymity in
WSNs. In most of real life applications of WSNs, sensor networks are having the location
information about various events and this information needs to secure or anonymous. In that method
the statistical framework is presented, this is based on the binary hypothesis testing for modeling,
analyzing, and evaluating statistical source anonymity WNS. The concept of notion of interval in
distinguish ability in order to model source anonymity, however this method fails to satisfy the
notion of interval in distinguish ability practically. Therefore, in this paper we are further extending
this method address such issues and practically prove its efficiency, we proposed a modification to
existing solutions to improve their anonymity against correlation tests
Index Terms: Attackers, Source Location, Privacy Preservation, Source Anonymity,
Sensor Network, Energy Consumption.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 5, May (2014), pp. 120-128
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

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II. INTRODUCTION
Recently we have studied in Toward a Statistical Framework for Source Anonymity in
Sensor Networks. In most of applications the locations of events reported by a sensor network need
to remain anonymous. That is, unauthorized observers must be unable to detect the origin of such
events by analyzing the network traffic, Security of wireless sensor networks, with variety of
techniques based on different adversarial assumptions it introduces the notion of “interval in
distinguish ability” and provides a quantitative measure to model anonymity in wireless sensor
networks; second, it maps source anonymity to the statistical problem of binary hypothesis testing
with nuisance parameters. In which how mapping source anonymity to binary hypothesis testing with
nuisance parameters leads to converting the problem of exposing private source information into
searching for an appropriate data transformation that removes or minimize the effect of the nuisance
information. Transform the problem from analyzing real-valued sample points to binary codes,
which opens the door for coding theory to be incorporated into the study of anonymous sensor
networks. In Proposed system propose a quantitative measure to evaluate statistical source
anonymity in sensor networks.
Sensor networks are deployed to sense, monitor, and report events of interest in a wide range
of applications including, but are not limited to, military, health care and animal tracking [1], [2], [3].
In many applications, such monitoring networks consist of energy constrained nodes that are
expected to operate over an extended period of time, making energy efficient monitoring an
important feature for unattended networks. In such scenarios, nodes are designed to transmit
information only when a relevant event is detected (i.e., event-triggered transmission). Consequently,
given the location of an event-triggered node, the location of a real event reported by the node can be
approximated within the node’s sensing range. The locations of the combat vehicle at different time
intervals can be revealed to an adversary observing nodes transmissions.
There are three parameters that can be associated with an event detected and reported by a
sensor node: the description of the event, the time of the event, and the location of the event. When
sensor networks are deployed in untrustworthy environments, protecting the privacy of the three
parameters that can be attributed to an event triggered transmission becomes an important security
feature in the design of wireless sensor networks. While transmitting the “description” of a sensed
event in a private manner can be achieved via encryption primitives, hiding the timing and spatial
information of reported events cannot be achieved via cryptographic means. The source anonymity
problem in wireless sensor networks is the problem of studying techniques that provide time and
location privacy for events reported by sensor nodes.
In the existing literature, the source anonymity problem has been addressed under two
different types of adversaries, namely, local and global adversaries. A local adversary is defined to
be an adversary having limited mobility and partial view of the network traffic.
Efficient power saving scheme and corresponding algorithm must be developed and designed in
order to provide reasonable energy consumption and to improve the network lifetime for wireless
sensor network systems. The cluster-based technique is one of the approaches to reduce energy
consumption in wireless sensor networks. In this article, we propose a optimize energy clustering
algorithm to provide efficient energy consumption in such networks. The main idea of this article is
to reduce data transmission distance of sensor nodes in wireless sensor networks by using the
uniform cluster concepts. In order to make an ideal distribution for sensor node clusters, we calculate
the average distance between the sensor nodes and take into account the residual energy for selecting
the appropriate cluster head nodes. The lifetime of wireless sensor networks is extended by using the
uniform cluster location and balancing the network loading among the clusters. Simulation results
indicate the superior performance of our proposed algorithm to strike the appropriate performance in
the energy consumption and network lifetime for the wireless sensor networks.

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WSN is nothing but the group of number of small sensor wireless devices which are
constrained for limited resource. These devices are also called as motes. In addition to this, WSN
consisting of one or few general purpose computing devices referred to as base stations (or sinks).
The basic use of WSN is to perform the monitoring of physical phenomenon for example light
monitoring, temperature monitoring, barometric monitoring etc over the geographical area of WSN
deployment. Each sensor node in WSN is having the functionalities such as battery, processing unit
and sensors. Each sensor node is having limitations of battery power which is means WSN is
resource constrained. On the other hand, the base stations in WSN are nothing but laptops
capabilities therefore those are not power constrained. The role of base station is bridge the
communication gap between the other networks and WSN. The real time applications of WSNs are
military applications, health applications, commercial applications, temperature monitoring etc [1].
The classification of WSNs is based on different aspects with main focus of designing the
secure routing protocol. The mobility of sensor nodes as well as base station is one aspect based on
which the WSNs are classified. In WSNs, the sensor nodes are either stationary or mobile based on
application requirements. Another reason of WSNs classification is topology used for nodes position.
The placements of nodes are either done manually or randomly by using existing methods of
topology. The total number of mobile nodes in WSN is also key based on which the WSNs are
classified [13]. Now days, privacy is main challenge of WSNs. This privacy is divided into two
classes such as contextual privacy and content oriented privacy. The contextual privacy is basically
concerning with the concerns the ability of adversaries to infer information from observations of
sensors and communications without access to the content of messages. Content-oriented privacy is
concerned with the ability of adversaries to learn the content of transmissions in the sensor network.
In contrast to content-oriented security, the issue of contextual privacy is concerned with protecting
the context associated with the dimensions and transmission of sensed data. For many scenarios,
general contextual information surrounding the sensor application, specially the location of the
message originator and the base station called as sink. Among the different security threats in
wireless sensor networks one is eavesdropping which involves attack against the confidentiality of
data that is being transmitted across the network. Various privacy-preserving routing techniques have
been developed for sensor networks. Most of them are designed to protect against the local
eavesdropper and some of them are capable of protecting against global eavesdropper [3] [7].
In the following section III we will discuss the different types of recommendation approaches
along with their advantages and disadvantages. Section IV presents the proposed approach.
III. LITERATURE REVIEW
In this section we are presenting the different methods those are presented to over the
problem of source location privacy in WSNs.
• In [4], Xi et al. proposed a new random walk routing method that reduces energy consumption
at the cost of increased delivery time. Path confusion has also been proposed as an anonymity-
preserving routing scheme by Hoh and Gruteser in [5].
• In [8], Ouyang et al. developed a scheme in which cycles are introduced at various points in the
route, potentially trapping the adversary in a loop and forcing the adversary to waste extra
resources.
• In [6], Wang et al. proposed a technique to maximize source location privacy by designing
routing protocols that distribute message flows to different routes. However, in the global
adversarial model, in which the adversary has access to all transmissions in the network,
routing-based schemes are insufficient to provide location privacy.

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• In [7], the global adversarial model was first introduced by Mehta et al. The authors motivated
the problem, analyzed the security of existing routing-based schemes under the new model, and
proposed two new schemes. In the first scheme, some sensor nodes act as fake sources by
mimicking the behavior of real events. For example, if the network is deployed to track an
animal, the fake sources could send fake messages with a distribution resembling that of the
animal’s movements. This, however, assumes some knowledge of the time distribution of real
events. In the second scheme, packets (real and fake) are sent either at constant intervals or
according to a predetermined probabilistic schedule. Although this scheme provides perfect
location privacy, it also introduces undesirable performance characteristics, in the form of
either relatively high delay or relatively high communication and computational overhead.
• In [9], Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, presented the survey over
wireless sensor networks which includes the flexibility, fault tolerance, high sensing fidelity,
low-cost and rapid deployment characteristics of sensor networks create many new and
exciting application areas for remote sensing. However, realization of sensor networks needs to
satisfy the constraints introduced by factors such as fault tolerance, scalability, cost, hardware,
topology change, environment and power consumption. Since these constraints are highly
stringent and specific for sensor networks, new wireless ad hoc networking techniques are
required.
• In [10], T. Arampatzis, J. Lygeros, and S. Manesis, Wireless sensors and wireless sensor
networks have come to the forefront of the scientific community recently. This is the
consequence of engineering increasingly smaller sized devices, which enable many
applications. The use of these sensors and the possibility of organizing them into networks have
revealed many research issues and have highlighted new ways to cope with certain problems.
In this paper, different applications areas where the use of such sensor networks has been
proposed are surveyed.
• In [11], Shao et al. introduced the notion of statistically strong source anonymity in which a
global adversary with ability to monitor the traffic in the entire network is unable to infer
source locations by performing statistical analysis on the observed traffic. In order to realize
their notion of statistical anonymity, nodes are programmed to transmit fake events according
to pre-specified distribution. The goal in [11], however, is to minimize the latency of reporting
real events while maintaining statistical in distinguish ability between real and fake
transmissions.
• In [12], [13], techniques are proposed to reduce the amount of traffic in the network that is due
to the transmission of fake events, techniques based on node proxies and data aggregation. In
such techniques, the overall communication overhead is reduced by making intermediate nodes
act as proxies that filter out fake messages or by aggregating multiple messages in a single
transmission. Such approaches make schemes based on generating fake messages more
attractive by mitigating the high communication overhead issue.
• In [14], Shao et al. also consider the problem of an active adversary. Their adversary also has
the ability to perform node compromise attacks, and they develop tools to prevent the adversary
from gaining access to event data stored in a node even if the adversary possesses that node’s
secret keys.
• In [15], Li and Ren proposed a scheme to provide both content confidentiality and source-
location privacy through routing to a randomly selected intermediate node (RRIN) and a
network mixing ring (NMR), where the RRIN provides local source location privacy and NMR
yields network-level (global) source location privacy.
• In [16], Ouyang et al. proposed four schemes: naive, global, greedy, and probabilistic to protect
the source location against global adversaries in.

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• In [17], Abbasi et al. proposed a distributed algorithm to mix real event traffic with carefully
chosen dummy traffic to hide the real event traffic pattern.
IV. METHODOLOGY AND PROPOSED ALGORITHM
• Introduce the notion of “interval in distinguish ability” and illustrate how the problem of
statistical source anonymity can be mapped to the problem of interval in distinguish ability.
• Propose a quantitative measure to evaluate statistical source anonymity in sensor networks.
• Map the problem of breaching source anonymity to the statistical problem of binary hypothesis
testing with nuisance parameters.
• Demonstrate the significance of mapping the problem in hand to a well-studied problem in
uncovering hidden vulnerabilities. In particular, realizing that the SSA problem can be mapped
to the hypothesis testing with nuisance parameters implies that breaching source anonymity can
be converted to finding an appropriate data transformation that removes the nuisance
information.
MODELING ANONYMITY
In this section we introduce our anonymity model for Wireless sensor networks. Intuitively,
anonymity should be measured by the amount of information about sources’ locations an adversary
can infer by monitoring the sensor network. The challenge, however, is to come up with an
appropriate model that captures all possible sources of information leakage and a proper way of
quantifying anonymity in different systems.
We start here by stating our assumptions about the network structure and the adversary’s
capabilities. We will then describe the currently used notion for source anonymity in sensor networks
and point out a source of information leakage that is undetectable by this notion.
Then, we will give a formal deﬁnation of a stronger Anonymity notion that, in addition to
capturing the sources of information leakage captured by the current notion, captures the source of
information leakage that was missed by the current notion, finally, we propose a quantitative
measure for evaluating the security of anonymous sensor networks.
NETWORK MODEL
We assume that communications take place in a network of energy constrained sensor nodes.
That is, nodes are assumed to be powered with unchangeable batteries, thus, conserving nodes
energy is a design requirement. Nodes are also equipped with a semantically secure encryption
algorithm, so that computationally bounded adversaries are unable to distinguish between real and
fake transmission by means of cryptographic tests. When a node detects an event, it places
information about the event in a message and broadcasts an encrypted version of the message.
ADVERSARIAL MODEL
Our adversary is similar to the one considered in [6], [7], in that it is external, passive, and
global. By external, we mean that the adversary does not control any of the nodes in the network and
also has no control over the real event process. By passive, we mean that the adversary is capable of
eavesdropping on the network, active attacks are not considered. By global, we mean that the
adversary can simultaneously monitor the activity of all nodes in the network. In particular, the
adversary can observe the timing and origin of every transmitted message. As opposed to a global
adversary, a local adversary is only capable of eavesdropping over a small area, typically the area
surrounding the base station, and attempts to determine the source of traffic by examining the packet
routing information or trying to follow the packets back to their source. Protocols that attempt to

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disguise the source of traffic through routing, while highly secure against local adversaries, do not
defend against global adversaries [6].
We also assume that the adversary is capable of storing large amount of message traffic data
and performing complex statistical tests. Furthermore, the adversary is assumed to know the
distribution of fake message transmissions. The only information unknown to the adversary is the
timing when real events occur.
Messages for the duration of that time interval. Assume now that a moving platoon is in the
vicinity of this node and the node started to report location information about the moving platoon.
The enemy does not need to distinguish between individual transmissions to infer the existence of
the moving platoon. All that is needed is the ability to distinguish between the time interval when no
real activity is reported and the time interval when the platoon is in the vicinity of the sensor node.
Consequently, in many real life applications, modeling
Source anonymity in sensor networks by the adversary’s ability to distinguish between individual
event transmissions is insufficient to guarantee location privacy. This fact calls for a stronger model
to properly address source anonymity in sensor networks. Before we proceed to the new anonymity
model, we formally define the currently adopted notion to model Anonymity in sensor networks,
namely, event in distinguish ability.
Definition 1 (Event In distinguish ability - ‘EI’): Events Reported by sensor nodes are said to be in
distinguishable if the inter-transmission times between them cannot be distinguished with significant
confidence by means of statistical tests.
Interval In distinguish ability (II)
The main goal of source location privacy systems is to hide the existence of real events. This
implies that, an adversary observing a sensor node during different time intervals, at which some of
the intervals include the transmission of real events and the others do not, should not be able to
determine with significant confidence which of the intervals contain real traffic. This leads to the
notion of interval in distinguish ability that will be essential for our anonymity formalization.
Definition 2 (Interval In distinguish ability - ‘II’): Let IF denotes a time interval with only fake event
Transmissions (call it the “fake interval”), and IR denotes time interval with some real event
transmissions (call it the “real interval”). The two time intervals are said to be statistically
indistinguishable if the distributions of inter-transmission times during these two intervals cannot be
distinguished by means of statistical tests. To model interval in distinguish ability.
Analysis of EI-based Approaches
In this section we analyze, using our proposed model, Systems that were shown to be secure
under event in distinguish ability; i.e., EI-based systems, We provide theoretical analysis showing
that real and fake intervals can be statistically distinguishable. Then, we simulate an existing scheme
to show that the simulation results coincide with the analytical results, and to quantify the anonymity
ofthe simulated design. We start by a recapitulation of EI based approaches for providing source
anonymity in sensor networks.
EI-based Approaches
Recall that nodes are designed to transmit fake messages according to a pre-specified
distribution. Furthermore, nodes store a sliding window of times between consecutive transmissions,
say X1; X2; _ _ _ ; Xk, where Xiis the random variable representing the time between the ith and the
i+1st transmissions and k is the length of the sliding window. When a real event occurs, its
transmission time, represented by Xk+1, is defined to be the smallest value such that the sequence

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X2;X3; _ _ _ ;Xk+1 passes some statistical goodness of fit tests. That is, an adversary observing the
sequence of inter-transmission times will observe a sequence that is statistically in distinguishable
from an iid sequence of random variables with the pre-specified Distribution of fake message
transmissions, However, by continuing in this fashion, the mean will Skew since nodes always favor
shorter intervals to transmit real events. To adjust the mean, the next transmission following a real
one, Xk+2 in this example, will be delayed. Again, the delay is determined so that the sequence in
the sliding window satisfies some statistical goodness of fittest. Consequently, as shown in [7], an
adversary observing the sensor node cannot differentiate between real and fake transmissions.
Theoretical Interval Distinguish ability
As discussed in Section 3, when an adversary can distinguish between real and fake intervals,
source location can be exposed, even if the adversary cannot distinguish between individual
transmissions. In what follows, we give theoretical analysis of interval in distinguish ability in EI
based systems. Let Xi be the random variable representing the time between the ith and the i + 1st
transmissions and let E[Xi] .We will demonstrate an adversary’s strategy of detecting the source
location by investigating two intervals, a fake interval and a real one.
Binary Hypothesis testing with nuisance parameters: In binary hypothesis testing, given two
hypothesis, H0 and H1, and a data sample that belongs to one of the two hypotheses (e.g., a bit
transmitted through a noisy communication channel), the goal is to decide to which hypothesis the
data sample belongs. In the statistical strong anonymity problem under interval in distinguish ability,
given an interval of inter transmission times; the goal is to decide whether the interval is fake or real.
Quantitative Measure: Our aim to find a security measure that can formally quantify the anonymity
of different systems. Let’s denote any adversarial strategy for breaching the anonymity of the system.
Fake Interval (IF): Recall that, in the absence of real events, nodes are programmed to transmit iid
fake messages according to a pre-specified probability distribution Real Interval (IR): By
definition, real intervals will have both fake and real transmissions. Let Ei be the random variable.
Representing the type of the event reported in the it transmission, i.e., fake or real.
V. EXPERIMENTAL ANALYSIS
There are three parameters that can be associated with an event detected and reported by a
sensor node: the description of the event, the time of the event, and the location of the event. When
sensor networks are deployed in untrustworthy environments, protecting the privacy of the three
parameters that can be attributed to an event triggered transmission becomes an important security
feature in the design of wireless sensor networks. Transmitting the “description” of a Sensed event in
a private manner can be achieved via Encryption primitives, hiding the timing and spatial
Information of reported events.
PR > PF PR < PF PR = PF Anonymity
Bound
Statistical goodness of fit based approach
(without nuisance)
7,301 2,695 4 0.539
Statistical goodness of fit based approach
(with nuisance)
5,076 4,924 0 0.984
Our Modified Approach ( without
nuisance)
5,161 4,832 7 0.967
Proposed Approach 5,234 4,912 6 0.946

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X axis = No. of nodes
Y axis = Anonymity bound in sec
Figure: Result Graph
VI. CONCLUSION AND FUTURE WORK
In this paper, provided a statistical framework based on binary hypothesis testing for
modeling, analyzing, and evaluating statistical source anonymity in wireless sensor networks. Also
introduced the notion of interval in distinguish ability to model source location privacy, Show that
the current approaches for designing statistically anonymous systems introduce correlation in real
intervals while fake intervals are uncorrelated. By mapping the problem of detecting source
information to the statistical problem of binary hypothesis testing with nuisance parameters, also
showed why previous studies were unable to detect the source of information leakage that was
demonstrated in this paper. Finally, in proposed a modification to existing solutions to improve their
anonymity against correlation tests.
VII. REFERENCE
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