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50120140505014
50120140505014
50120140505014
50120140505014
50120140505014
50120140505014
50120140505014
50120140505014
50120140505014
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50120140505014

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  • 1. 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
  • 2. 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 121 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.
  • 3. 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 122 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.
  • 4. 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 123 • 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.
  • 5. 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 124 • 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
  • 6. 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 125 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
  • 7. 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 126 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
  • 8. 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 127 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 [1] Basel Alomair, Member, IEEE, Andrew Clark, Student Member, IEEE, Jorge Cuellar, and Radha Poovendran, Senior Member, IEEE, “Toward a Statistical Framework for Source Anonymity in Sensor Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 12, NO. 2, FEBRUARY 2013. [2] B. Alomair, A. Clark, J. Cuellar, and R. Poovendran, “Statistical Framework for Source Anonymity in Sensor Networks,” Proc. IEEE GlobeCom, 2010. [3] B. Alomair, A. Clark, J. Cuellar, and R. Poovendran, “On Source Anonymity in Wireless Sensor Networks,” Proc. IEEE/IFIP 40th Int’l Conf. Dependable Systems and Networks (DSN ’10), 2010. [4] Y. Xi, L. Schwiebert, and W. Shi, “Preserving Source Location Privacy in Monitoring-Based Wireless Sensor Networks,” Proc. IEEE 20th Int’l Parallel & Distributed Processing Symp. (IPDPS ’06), pp. 1-8, 2006.
  • 9. 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 128 [5] B. Hoh and M. Gruteser, “Protecting Location Privacy Through Path Confusion,” Proc. IEEE/CreatNet First Int’l Conf. Security and Privacy for Emerging Areas in Comm. Networks (SecureComm ’05), pp. 194-205, 2005. [6] H. Wang, B. Sheng, and Q. Li, “Privacy-Aware Routing in Sensor Networks,” Elsevier J. Computer Networks, vol. 53, no. 9, pp. 1512-1529, 2009. [7] K. Mehta, D. Liu, and M. Wright, “Location Privacy in Sensor Networks against a Global Eavesdropper,” Proc. IEEE 15th Int’l Conf. Network Protocols (ICNP ’07), pp. 314-323, 2007. [8] Y. Ouyang, Z. Le, G. Chen, J. Ford, F. Makedon, and U. Lowell, “Entrapping Adversaries for Source Protection in Sensor Networks,” Proc. IEEE Seventh Int’l Symp. World of Wireless, Mobile and Multimedia Networks (WOWMOM ’06), pp. 32-41, 2006. [9] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: A Survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, 2002. [10] Arampatzis, J. Lygeros, and S. Manesis, “A Survey of Applications of Wireless Sensors and Wireless Sensor Networks,” Proc. IEEE 13th Mediterranean Conf. Control and Automation (MED ’05), pp. 719-724, 2006. [11] M. Shao, Y. Yang, S. Zhu, and G. Cao, “Towards Statistically Strong Source Anonymity for Sensor Networks,” Proc. IEEE INFOCOM, pp. 466-474, 2008. [12] Y. Yang, M. Shao, S. Zhu, B. Urgaonkar, and G. Cao, “Towards Event Source Unobservability with Minimum Network Traffic in Sensor Networks,” Proc. First ACM Conf. Wireless Network Security (WiSec ’08), pp. 77-88, 2008. [13] W. Yang and W. Zhu, “Protecting Source Location Privacy in Wireless Sensor Networks with Data Aggregation,” Proc. Seventh Int’l Conf. Ubiquitous Intelligence and Computing (UIC ’10), pp. 252-266, 2010. [14] M. Shao, S. Zhu, W. Zhang, G. Cao, and Y. Yang, “pDCS: Security and Privacy Support for Data-Centric Sensor Networks,” IEEE Trans. Mobile Computing, vol. 8, no. 8, pp. 1023-1038, Aug. 2009. [15] Y. Li and J. Ren, “Preserving Source-Location Privacy in Wireless Sensor Networks,” Proc. IEEE Sixth Ann. Comm. Soc. Conf. Sensor, Mesh and Ad Hoc Comm. and Networks (SECON ’09), pp. 493-501, 2009. [16] Y. Ouyang, Z. Le, D. Liu, J. Ford, and F. Makedon, “Source Location Privacy against Laptop-Class Attacks in Sensor Networks,” Proc. Fourth Int’l Conf. Security and Privacy in Comm. Networks (SecureComm ’08), pp. 1-10, 2008. [17] A. Abbasi, A. Khonsari, and M. Talebi, “Source Location Anonymity for Sensor Networks,” Proc. IEEE Sixth Conf. Consumer Comm. and Networking Conf. (CCNC ’09), pp. 588-592, 2009. [18] Preetee K. Karmore, Supriya S. Thombre and Gaurishankar L. Girhe, “Review on Operating Systems and Routing Protocols for Wireless Sensor Networks”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 331 - 339, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [19] K.Sangeetha and Dr. K.Ravikumar, “A Framework For Practical Vulnerabilities of the Tor (The Onion Routing) Anonymity Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 112 - 120, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [20] Revathi Venkataraman, K.Sornalakshmi, M.Pushpalatha and T.Rama Rao, “Implementation of Authentication And Confidentiality In Wireless Sensor Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 553 - 560, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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