Privacy-preserving data aggregation in resource-constrained sensor nodes in Internet of Things: A review

Ali, Inayat; Khan, Ahmad Salman; Sabir, Sonia

doi:10.1016/j.fcij.2017.11.004

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…In IoT, several existing privacy preservation mechanisms of data aggregation fall in different categories like anonymity based privacy preservation [45], encryption based privacy preservation [90]. The design of IoMT based privacy preserved scheme with significant data utility still a critical challenge for future research [91].…”

Section: B Privacy Of Data For Information Exchangementioning

confidence: 99%

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

et al. 2021

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Internet of medical things (IoMT) is getting researchers' attention due to its wide applicability in healthcare. Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes. Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers. We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature. This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered. A taxonomy is presented to categorize the schemes. Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio. We present a summary of these scheme along with analytical discussion. Finally, a number of open research challenges are identified. Moreover, the schemes are explored to identify the challenges that are addressed in each scheme.

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Section: B Privacy Of Data For Information Exchangementioning

confidence: 99%

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

et al. 2021

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“…A comprehensive survey of artificial intelligence-based classification for edge intelligence was presented in [17]. A review of privacy preservation mechanisms for resource-oriented sensors in IoT was investigated in [18]. The influence of IoT in healthcare globally was proposed in [19] with the new transformative computing in progress, different types of signals acquired for IoT seconds, data processing between sensors and users poses a significant threat in healthcare data analytics concerning response time.…”

Section: Related Workmentioning

confidence: 99%

Sugeno Fuzzy Model Free Reinforced Transformation Based Data Processing for Healthcare Sources in IoT Environment

Sellam¹,

Natarajan²,

Gopu³

et al. 2021

Preprint

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Modern computer sciences and information technologies are anticipated to bring transformative influence in part that mobile communication technologies play in society. To completely take advantage of the services bestowed by modern computer sciences and information technologies, the evidence of the economic and business case is an essential prerequisite. Existing research engulfs several transformative computing methods based on sensors area obtainable as service contain optimize resource management, data processing/storage and security provisioning. With transformative computing being on edge, real-time data must be necessitated for healthcare data analytics. The conventional cloud server cannot address the latency requirements of healthcare IoT sensors. To survive with how to handle these services, we introduce a hybrid method integrating Sugeno Fuzzy Inference (SFI) and Model-free Reinforcement Learning to enhance healthcare IoT and cloud latency. The objective is to lessen high latency between healthcare IoT devices. The proposed Sugeno Fuzzy Model-free Reinforcement Learning Data Computing (SF-MRLDC) method uses a Sugeno Fuzzy Inference model integrated with a Model-free Reinforcement Learning model data computing in a healthcare IoT data analytics environment. The simulation results of the SF-MRLDC method show that it is computationally efficient in terms of latency by ensuring better response time.

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“…Computation Cost, Privacy level and Privacy against aggregator IoT sensor node [10]. Computational Cost (CC): It is the evaluation of algorithm in terms of complexity of mathematical iterations used.…”

Section: Comparative Analysismentioning

confidence: 99%

Privacy-Preserving Data Aggregation in Internet of Things (Iot): An Overview

Mohapatra¹,

Sheetlani²,

Patra³

2020

ijarcs

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IoT devices enhance efficiency, accuracy and economic advantages along with less involvement of human resources, thus our different daily applications have become more flexible and convenient. But, in IoT we have many security and privacy challenges emerging on regular basis. Earlier this issue has been addressed by introduction of many approaches for achieving privacy-preserving in data aggregation process. In this aspect this paper presents an outline of IoT-oriented approach for achieving privacy preservation together with minimizing communication overhead. This paper reviews the latest Privacy Preserving Data Aggregation (PPDA) techniques along with their comparative analysis. Latest techniques are investigated here to give a detail analysis of the each and every step of these techniques. In addition, every mathematical operation used in several PPDA schemes is analyzed here. Also current study will be advantageous to researchers in designing solutions in terms of energy efficiency and computational feasibility for ensuring user privacy in different IoT application.

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Privacy-preserving data aggregation in resource-constrained sensor nodes in Internet of Things: A review

Cited by 18 publications

References 18 publications

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Sugeno Fuzzy Model Free Reinforced Transformation Based Data Processing for Healthcare Sources in IoT Environment

Privacy-Preserving Data Aggregation in Internet of Things (Iot): An Overview

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