Energy-efficient privacy-preserving data aggregation protocols based on slicing

Liu, Xiaowu; Yu, Jiguo; Zhang, Xiaowei; Zhang, Qiang; Fu, Can

doi:10.1186/s13638-020-1643-6

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…[31], Kong, et al [32], Liu, et al [33], and Liu, et al [34] have all proposed protocols for data aggregation in the smart grid. In the future, secure multi-party computation [14][15][16]35] may also be useful for data aggregation.…”

Section: Related Workmentioning

confidence: 99%

A secure and effective data aggregation in WSN for improved security and data privacy

Mary,

Murugeswari,

Shri

et al. 2023

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The increasing prevalence of internet usage and mobile devices has underscored the critical importance of safeguarding personal data. This is especially important in Wireless Sensor Networks (WSNs), where information typically requires in-network computing and collaborative processing. These computationally demanding approaches are not suitable for resource-constrained WSN nodes. Aggregating data effectively while protecting user data is a major challenge in wireless sensor networks. Many privacies of homomorphism encryption-based WSN data aggregation methods have been created and investigated recently. Since cluster leaders (aggregators) may rapidly combine cypher texts without decryption, communication overhead is reduced, making these data aggregation methods more secure than traditional ones. However, the base station only receives aggregated output, causing issues. Initial limits apply to aggregating functions. If the aggregated output is the sum of sensing data, the base station cannot acquire the maximum value. Second, attaching message digests or signatures to sensory samples does not allow the base station to validate data authenticity. In dangerous places, WSNs must be energy-efficient and private. In this research, we present a data aggregation method known as Energy-Efficient and Privacy-Preserving (E2P2). E2P2 data aggregation utilizes less energy and yields more accurate results. Private data aggregation with increased accuracy and hybrid encryption is presented in this research. The goal is to reduce data transmission and energy use collisions and offset collision-induced loss. Extensive simulations compare E2P2 to earlier approaches. Experimental results show that E2P2 outperforms other algorithms. Good exactness, complexity, and safety are demonstrated by theoretical and simulation results.

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Section: Related Workmentioning

confidence: 99%

A secure and effective data aggregation in WSN for improved security and data privacy

Mary,

Murugeswari,

Shri

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The SECPDA method, in contrast, has a poor throughput, a moderate energy consumption, and a considerable latency. An energy-efficient, privacy-preserving data aggregation method based on slicing or the EPPA model is suggested by the study described in [2]. This protocol aims to minimize the wait time.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

Motghare

Nair

2023

IJRITCC

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The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance.

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“…slicing SDA, confidence SDA, encryption SDA, privacy SDA, and anomaly detection SDA, security goals, and network topologies. To save communication costs and protect private data, data decomposition technique is implemented instead of slicing in privacy-preserving data aggregation protocols [36].…”

Section: Security and Mobility In Data Aggregation Wsns Andmentioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

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Energy-efficient privacy-preserving data aggregation protocols based on slicing

Cited by 12 publications

References 28 publications

A secure and effective data aggregation in WSN for improved security and data privacy

A secure and effective data aggregation in WSN for improved security and data privacy

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

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