Towards understanding source location privacy in wireless sensor networks through fake sources

Source location privacy is becoming an increasingly important property in wireless sensor network applications, such as asset monitoring. The original source location problem is to protect the location of a source in a wireless sensor network from a single distributed eavesdropper attack. Several techniques have been proposed to address the source location problem, where most of these apply some form of traffic analysis and engineering to provide enhanced privacy. One such technique, namely fake sources, has proved to be promising for providing source location privacy. Recent research has concentrated on investigating the efficiency of fake source approaches under various attacker models. In this paper, we (i) provide a novel formalisation of the source location privacy problem, (ii) prove the source location privacy problem to be NP-complete, and (iii) provide a heuristic that yields an optimal level of privacy under appropriate parameterisation. Crucially, the results presented show that fake sources can provide a high, sometimes optimal, level of privacy

An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

open access articleWireless Sensor Network is a network of large number of nodes with limited power and computational capabilities. It has the potential of event monitoring in unattended locations where there is a chance of unauthorized access. The work that is presented here identifies and addresses the problem of eavesdropping in the exposed environment of the sensor network, which makes it easy for the adversary to trace the packets to find the originator source node, hence compromising the contextual privacy. Our scheme provides an enhanced three-level security system for source location privacy. The base station is at the center of square grid of four quadrants and it is surrounded by a ring of flooding nodes, which act as a first step in confusing the adversary. The fake node is deployed in the opposite quadrant of actual source and start reporting base station. The selection of phantom node using our algorithm in another quadrant provides the third level of confusion. The results show that Dissemination in Wireless Sensor Networks (DeLP) has reduced the energy utilization by 50% percent, increased the safety period by 26%, while providing a six times more packet delivery ratio along with a further 15% decrease in the packet delivery delay as compared to the tree-based scheme. It also provides 334% more safety period than the phantom routing, while it lags behind in other parameters due to the simplicity of phantom scheme. This work illustrates the privacy protection of the source node and the designed procedure may be useful in designing more robust algorithms for location privac

On the tradeoff between privacy and energy in wireless sensor networks

Source location privacy is becoming an increasingly important property of some wireless sensor network applica- tions. The fake source technique has been proposed as an approach for handling the source location privacy problem in these situations. However, whilst the efficiency of the fake source techniques is well documented, there are several factors that limit the usefulness of current results: (i) the assumption that fake sources are known a priori, (ii) the selection of fake sources based on an prohibitively expensive pre-configuration phase and (iii) the lack of a commonly adopted attacker model. In this paper we address these limitations by investigating the efficiency of the fake source technique with respect to possible implementations, configurations and extensions that do not require a pre-configuration phase or a priori knowledge of fake sources. The results presented demonstrate that one possible implementation, in presence of a single attacker, can lead to a decrease in capture ratio of up to 60% when compared with a flooding baseline. In the presence of multiple attackers, the same implementation yields only a 30% decrease in capture ratio with respect to the same baseline. To address this problem we investigate a hybrid technique, known as phantom routing with fake sources, which achieves a corresponding 50% reduction in capture ratio

A dynamic fake source algorithm for source location privacy in wireless sensor networks

Wireless sensor networks (WSNs) are commonly used in asset monitoring applications, where it is often desirable for the location of the asset being monitored to be kept private. The source location privacy (SLP) problem involves protecting the location of a WSN source node from an attacker who is attempting to locate it. Among the most promising approaches to the SLP problem is the use of fake sources, with much existing research demonstrating their efficacy. Despite the effectiveness of the approach, the most effective algorithms providing SLP require network and situational knowledge that makes their deployment impractical in many contexts. In this paper, we develop a novel dynamic fake sources-based algorithm for SLP. We show that the algorithm provides state-of-the-art levels of location privacy under practical operational assumptions

Assessing the performance of phantom routing on source location privacy in wireless sensor networks

As wireless sensor networks (WSNs) have been applied across a spectrum of application domains, the problem of source location privacy (SLP) has emerged as a significant issue, particularly in safety-critical situations. In seminal work on SLP, phantom routing was proposed as an approach to addressing the issue. However, results presented in support of phantom routing have not included considerations for practical network configurations, omitting simulations and analyses with larger network sizes. This paper addresses this shortcoming by conducting an in-depth investigation of phantom routing under various network configurations. The results presented demonstrate that previous work in phantom routing does not generalise well to different network configurations. Specifically, under certain configurations, it is shown that the afforded SLP is reduced by a factor of up to 75

Improving Source Location Privacy in Social Internet of Things Using a Hybrid Phantom Routing Technique

The amalgamation of Smart IoT and Machine learning is an emerging research area. In this context, a new trend in IoT called Social IoT has been considered for this study. The Social IoT has benefits of connectivity exhibited within the network of connected objects through the Internet of Things (IoT). It covers the entire world and provides innovative services to improve life standards, establishes novel businesses, and makes buildings and cities. Certain smart things allow the collection of ubiquitous data or traffic, which pose a threat to source location privacy. Therefore, it limits the source of the Internet of Things vision if implemented wrongly. These threats come along with some challenges, adversary profiles, and the location privacy of personal data. When they are used to monitor important assets, the attacker can easily hunt the location of these assets. However, the source location constitutes a way to prevent the adversary from finding the location of the source. This research has used a hybrid phantom method by combining the phantom node and multi-path route that improves privacy and reduces the consumption of energy. The Analytic Hierarchal Process (AHP) is used for phantom node selection, based on parameters such as energy, distance, heterogeneity, and neighbor list. The result shows the average consistency value of the parameters is 4.2 and the consistency index value is 0.066. The overall priority of the alternative node is 2.089 as compared to other nodes. The sum of the vector weight value is obtained as 4.845. The total average energy consumption is 1.211 J and the average safety period capture ratio is 59.41%. The proposed techniques overwhelmed the deficiencies in existing techniques, reduces energy consumption improves the safety period and increases the network lifetime

A decision theoretic framework for selecting source location privacy aware routing protocols in wireless sensor networks

Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Many routing protocols have been proposed that provide SLP, all of which provide a trade-off between SLP and energy. Experiments have been conducted to gauge the performance of the proposed protocols under different network parameters such as noise levels. As that there exists a plethora of protocols which contain a set of possibly conflicting performance attributes, it is difficult to select the SLP protocol that will provide the best trade-offs across them for a given application with specific requirements. In this paper, we propose a methodology where SLP protocols are first profiled to capture their performance under various protocol configurations. Then, we present a novel decision theoretic procedure for selecting the most appropriate SLP routing algorithm for the application and network under investigation. We show the viability of our approach through different case studies

Quantifying Source Location Privacy Routing Performance via Divergence and Information Loss

Source location Privacy (SLP) is an important property for security critical applications deployed over a wireless sensor network. This property specifies that the location of the source of messages needs to be kept secret from an eavesdropping adversary that is able to move around the network. Most previous work on SLP has focused on developing protocols to enhance the SLP imparted to the network under various attacker models and other conditions. Other works have focused on analysing the level of SLP being imparted by a specific protocol. In this paper, we introduce the notion of a routing matrix which captures when messages are first received. We then introduce a novel approach where an optimal SLP routing matrix is derived. In this approach, the attacker's movement is modelled as a Markov chain where measures of conditional entropy and divergence are used to compare routing matrices and quantify if they provide high levels of SLP. We propose the notion of a properly competing paths that causes an attacker to divert when moving towards the source. This concept provides the basis for developing a perturbation model, similar to those used in privacy-preserving data mining. We formally prove that properly competing paths are both necessary and sufficient in ensuring the existence of an SLP-aware routing matrix and show their usage in developing an SLP-aware routing matrix. Further, we show how different SLP-aware routing matrices can be obtained through different instantiations of the framework. Those instantiations are obtained based on a notion of information loss achieved through the use of the perturbation model proposed

An Enhanced Communication Protocol for Location Privacy in WSN

Wireless sensor network (WSN) is built of many sensor nodes. The sensors can sense a phenomenon, which will be represented in a form of data and sent to an aggregator for further processing. WSN is used in many applications, such as object tracking and security monitoring. The objects in many situations need physical and location protection. In addition to the source location privacy, sink location privacy should be provided. Providing an efficient location privacy solution would be challenging due to the open nature of the WSN. Anonymity is a key solution for location privacy. We present a network model that is protected against local, multilocal, and global adversaries that can launch sophisticated passive and active attacks against the WSN.http://dx.doi.org/10.1155/2015/69709