A secure and efficient privacy-preserving data aggregation algorithm

Dou, Hui; Chen, Yuling; Yang, Yixian; Long, Yangyang

doi:10.1007/s12652-020-02801-6

Cited by 15 publications

(12 citation statements)

References 26 publications

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“…In this method, they have sliced the data and encrypted using the advanced encryption standard (AES) encryption to secure the communication between the sensor device and the IoT devices. In the paper, Dou et al [18], they have proposed an algorithm, secure and efficient privacy-preserving data aggregation algorithm (SECPDA), which provides privacy to the clustered data aggregation data. This algorithm selects the cluster head nodes and uses various slicing methods to provide privacy on the aggregated data.…”

Section: Figure 1 Data Aggregation In Wsnsmentioning

confidence: 99%

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Two phase secure data collection technique for wireless sensor networks

Thahniyath

Mishra

Moorthy

2022

IJEECS

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Wireless <span>sensor networks (WSNs) are the sensors that are dispersed in a different location that can sense the accumulated data in real-time and send it to the central location for the process of data aggregation. During the transfer of the information using the data nodes from the WSNs to the central location, there may be chances that the data node could be compromised by sensor failure or by an attack from a malicious user. To overcome this problem, we propose a two-phase secure data collection (TPSDC) technique for wireless sensor networks which provides confidentiality and integrity for the data nodes that are being sent to the central location and also during the data aggregation in the central location. Various existing methods have been proposed to secure the data when sent from the WSNs to the internet of things (IoT) devices but they lack to provide both confidentiality and integrity at the same time. Hence our model provides both integrity and confidentiality by providing security to the data nodes. Experimental results show that our model TPSDC performs better in terms of misclassification rate, detection rate, throughput, network lifetime analysis of the node, and communication overhead of the node when compared with the existing methods.</span>

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Section: Figure 1 Data Aggregation In Wsnsmentioning

confidence: 99%

“…after the selection of the cluster, the head has been evaluated, if any of the IoT devices don't have any trust value, then the probability of the IoT device is evaluated as (18):…”

Section: Secure Metric For Classifying Malicious Sensor Nodesmentioning

confidence: 99%

Two phase secure data collection technique for wireless sensor networks

Thahniyath

Mishra

Moorthy

2022

IJEECS

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“…Authors (Dou et al, 2021) have proposed a data aggregation privacy security method in wireless sensor networks (WSN). The proposed solution is a stable and efficient based on the secure and efficient privacy‐preserving data aggregation method which makes use of SEP protocol to choose the cluster head nodes in a dynamic manner.…”

Section: Related Workmentioning

confidence: 99%

A formal method for privacy‐preservation in cognitive smart cities

Khan

2021

Expert Systems

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The Internet of things (IoT) and communication technologies are enabling the consumer to use the smart devices. The explosion of smart devices is shifting the IoT into a framework, which we call the cognitive IoT. The cognitive IoT can enhance many sectors such as smart cities, healthcare, industry 4.0, transportation, just to name a few. Most of the data produced in smart cities are wasted because the important information is not extracted due to lack of standard mechanism for knowledge extraction and archiving methods. This has attracted the attention of researcher to design new approaches of machine and cognitive learning that can handle vast amount of dynamic data. The cognitive smart city is the integration of IoT, smart city technology, real-time big data analytics and artificial intelligence (AI) strategies for proactive actions. The services in smart cities relies on the collection and analysis of the data which are provided by the use themselves or accessed by the services providers. The citizen engagement is the key for success of smart city; however, the engagement may get reduced due to privacy concerns arising from data collection. Therefore, privacy-preservation shall be achieved in a manner where valuable data is exchanged with service provider, and other third party while protecting the citizens' privacy, upholding data laws and enforcement. Therefore, there is a need to control the anonymization and mix some more techniques to preserve the quality of the data.The proposed formal method for privacy-preservation in smart cities is based on pseudonymization, clustering, anonymization and differential privacy methods. The modified clustering algorithm selects the initial cluster based on the concept of dissimilarity between the data sequences. We have assessed the functional correctness and preformation of the proposed model for privacy-preservation in smart cities. The proposed method has lower discriminating rate as compared to other existing methods.

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“…Langari et al 26 proposed a combined anonymity algorithm based on k -means clustering to minimize information loss and protect user privacy in social networks. Dou et al 27 proposed a privacy protection data aggregation algorithm based on primitive clustering, which can effectively reduce data traffic and improve data privacy. Song et al 28 introduced a hierarchical clustering method based on a sensitivity level division algorithm, and proposed a sensitive k -anonymity model, which can effectively protect data privacy and improve data security and practicability.…”

Section: Related Workmentioning

confidence: 99%

MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things

Zhang

Tongtong

Sun

et al. 2021

International Journal of Distributed Sensor Networks

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The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user’s privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k-medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k-medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k-medoids clustering, multiple partition differential privacy k-medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user’s privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering.

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A secure and efficient privacy-preserving data aggregation algorithm

Cited by 15 publications

References 26 publications

Two phase secure data collection technique for wireless sensor networks

Two phase secure data collection technique for wireless sensor networks

A formal method for privacy‐preservation in cognitive smart cities

MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things

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