Staircase based differential privacy with branching mechanism for location privacy preservation in wireless sensor networks

Chakraborty, Bodhi; Verma, Shekhar; Singh, Krishna Pratap

doi:10.1016/j.cose.2018.03.002

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…• The manuscript conducts a thorough performance evaluation, considering multiple metrics to assess the efficiency and effectiveness of the LPWSN-DSRNN-MOA method. This contributes to a comprehensive understanding of the proposed approach's strengths and limitations [17,18]. In summary, the LPWSN-DSRNN-MOA method contributes novel combinations of neural network architecture, optimization algorithms, and pre-processing techniques to enhance the protection of sensitive location information in WSNs, demonstrating its efficacy through rigorous implementation and evaluation.…”

Section: A Contribution Statementmentioning

confidence: 94%

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Yalin Nie

2024

jes

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Wireless Sensor Networks (WSNs) play a pivotal role in modern data-driven applications, yet concerns persist regarding the privacy and security of sensitive location information. The attributes of Wireless Sensor Networks (WSNs) make them susceptible to eavesdropping, enabling attackers to intercept data packets across single or multiple communication links. This interception allows for the extraction of sensitive data from various sensor information, presenting a significant challenge to location privacy. Consequently, it becomes crucial to implement effective measures for safeguarding the privacy of training sample data when utilizing WSNs. In this manuscript, Dynamically Stabilized Recurrent Neural Network (DSRNN) optimized with Mother Optimization Algorithm (DSRNN-MOA) is proposed. Initially data is taken from WSN dataset. Afterward the data is fed to Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) based pre-processing process. The pre-processing output is provided to the Dynamically Stabilized Recurrent Neural Network to enhance source location protection while addressing challenges related to recurrent network stability and gradient issues. The learnable parameters of the DSRNN is optimized using MOA. The proposed strategy, LPWSN-DSRNN-MOA, is implemented in MATLAB, and its effectiveness is assessed using a number of performance evaluation measures, including ROC analysis, accuracy, precision, recall, f1-score, mean squad error, and recall. The proposed LPWSN-DSRNN-MOA method shows the highest accuracy of 98%, precision of 99%, specificity of 98% and F1-score of 99% while comparing other existing methods such as Location Protection for Wireless Sensor Networks based on Artificial Neural Network(LPWSN-ANN), Location Protection for Wireless Sensor Networks based on Deep Neural Network (LPWSN-DNN), and Location Protection for Wireless Sensor Networks based on Machine Learning (LPWSN-ML) respectively.

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Section: A Contribution Statementmentioning

confidence: 94%

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Yalin Nie

2024

jes

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“…With the increase of branches, the security period is greatly enhanced while the tree-shape route leads the additional energy overhead. Moreover, the staircase and branching scheme was introduced to prevent backtracking along the routing paths [10]. The stochastic and diffusive routing using multiple virtual source (SDR-m) technology was proposed [23].…”

Section: Related Workmentioning

confidence: 99%

“…And the fake packet injection is another well-known method against backtracking attacks. This method embeds fake traffic into real data making it difficult for attackers to distinguish real traffic from fake data and thus guarantees the security of source location [10]. Due to the reason that the fake source is generated statically, attackers can easily find the location of the fake source and thus discard it to find the real one [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Balanced Source Location Privacy Scheme Using Multibranch Path in WSNs for IoT

Wang

Liu

Zhao

et al. 2021

Wireless Communications and Mobile Computing

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Source location privacy, one of the core contents of Wireless Sensor Network (WSN) security, has a significant impact on extensive application of WSNs. In this paper, a novel location privacy protection routing scheme called Energy Balanced Branch Tree (EBBT) is proposed by using multibranch and fake sources. This scheme has three phases. In the first place, the data of the source are randomly sent to a certain intermediate node. Then, a minimum hop routing (MHR) from the intermediate node to the base station is formed. Then, branch paths with fake sources are generated dynamically from some nodes on the MHR path. Finally, a tree-shaped structure from real source nodes and fake source nodes to the base station is achieved. In difference to the previous schemes, the location of the real source in the EBBT scheme does not affect the location and the number of fake sources. During the formation of the tree-shaped multibranch paths, the residual energy of nodes is considered sufficiently, and the control of the direction of each branch path is also involved. The influence of the number and length of branches on the network lifetime and network security is also investigated. Experimental results show that the proposed algorithm has the advantages of long network security period and lifetime, as well as high path diversity. Our simulation further illustrates that the EBBT scheme has favorable privacy of the source location without changing the network lifetime.

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“…Source location privacy is an emerging area in event-driven applications such as military and wildlife tracking applications. At any time, the sensor node becomes a source node, and thus when event is detected by a node, the message for a specific event is transmitted to the sink node [3][4][5]. In many cases, the source node can be easily located through the shared wireless transmission medium.…”

Section: Introductionmentioning

confidence: 99%

C²S²‐LOOP: Circular Chessboard‐Based Secure Source Location Privacy Model Using ECC‐ALO in WSN

Jan¹,

Khan

Al-Bayatti

et al. 2021

Wireless Communications and Mobile Computing

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Source location privacy (SLP) is a serious issue in wireless sensor networks (WSN) since Eavesdroppers tries to determine the source location. Hunting Animals in Forest is considered as an example for SLP. Many conventional schemes have been proposed for SLP in WSN, namely, Random Walk Routing, and Fake Messages Transmission, which cause critical issues (less safety period, packet delivery latency, and high energy consumption). Furthermore, the security analysis is not properly investigated in any previous work. In this paper, we propose a new model called the circular chessboard-based secure source location privacy model (C2S2-LOOP) with the following tasks: key generation, network topology management (clustering), intercluster routing (travel plan), and data packets encryption. All sensor nodes are deployed in a circular chessboard (Circular Field) and the key generation ( P U K , S E K ) is invoked using elliptic curve cryptography (ECC) with Ant Lion Optimization algorithm, which mitigate the issues of conventional ECC. Then, the network topology is managed using clustering where residual energy of the nodes is used for Cluster Head (CH) selection. Intercluster routing is implemented using packet traversing using clockwise and anticlockwise directions, which are mainly concerned with establishing a secure route between the source to the destination node. To ensure data security, we present the Chaotic Artificial Neural Network (C-ANN) in which encryption is executed. Assume that CH near to the source node has a high trust value, then it traverses (clock-wise) real packets towards sink node and similarly in the left side region (anticlockwise), fake packets are transmitted. Network simulations (OMNeT++) are evaluated and compared with the previous approaches, and finally, our proposed scheme concludes that it maintains not only source node location privacy (large safety period) and also reduces energy consumption by more than 40% and latency by more than 35%.

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Staircase based differential privacy with branching mechanism for location privacy preservation in wireless sensor networks

Cited by 12 publications

References 17 publications

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Energy Balanced Source Location Privacy Scheme Using Multibranch Path in WSNs for IoT

C²S²‐LOOP: Circular Chessboard‐Based Secure Source Location Privacy Model Using ECC‐ALO in WSN

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Staircase based differential privacy with branching mechanism for location privacy preservation in wireless sensor networks

Cited by 12 publications

References 17 publications

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Energy Balanced Source Location Privacy Scheme Using Multibranch Path in WSNs for IoT

C2S2‐LOOP: Circular Chessboard‐Based Secure Source Location Privacy Model Using ECC‐ALO in WSN

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C²S²‐LOOP: Circular Chessboard‐Based Secure Source Location Privacy Model Using ECC‐ALO in WSN