Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

Ren, Wei; Xin, Tong; Du, Jing; Wang, Na; Li, Shancang; Min, Geyong; Zhao, Zhiwei

doi:10.1007/s10796-021-10116-w

Cited by 18 publications

(7 citation statements)

References 43 publications

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“…Ren et al. [14] proposed a stream data anonymisation method as a privacy‐enhancing technique for data collecting by IoT devices. However, privacy enhancement techniques have been proposed for various IoT streaming and media data scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, other applications use different types of security and privacy. For instance, Ontology and Anonymisation have been used for privacy in articles [3,14]. On the other hand, due to the mass production of data in the IoT, security and privacy are challenged.…”

Section: A Brief Summary Of the Evaluation Of Solutionsmentioning

confidence: 99%

“…This sub-section presents a short analysis of the security/privacy-based IoT applications shown in refs [3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19]…”

mentioning

confidence: 99%

See 2 more Smart Citations

Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Yaraziz

Jalili

Gheisari

et al. 2022

IET Circuits, Devices & Syst

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The Internet of Things (IoT) is a self-configuring, intelligent system in which autonomous things connect to the Internet and communicate with each other. As 'things' are autonomous, it may raise privacy concerns. In this study, the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserving privacy in IoT-based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications. Based on the results of our study, it is clear that new methods such as Blockchain, Machine Learning, Data Minimisation, and Data Encryption can greatly impact privacy issues to ensure security and privacy. Moreover, it makes sense that users can protect their personal information easier if there is fewer data to collect, store, and share by smart devices. Thus, this study proposes a machine learning-based data minimisation method that, in these networks, can be very beneficial for privacy-preserving.

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Section: Methodsmentioning

confidence: 99%

Section: A Brief Summary Of the Evaluation Of Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Yaraziz

Jalili

Gheisari

et al. 2022

IET Circuits, Devices & Syst

View full text Add to dashboard Cite

show abstract

“…Traditional data obfuscation methods are often ineffective and inefficient for time series datasets. For example, the perturbation method [23,24] is commonly used to mask sensitive data by randomly adding crafted noises. However, controlling the perturbation level to preserve privacy without sacrificing the original data's value is a challenging task.…”

Section: Related Workmentioning

confidence: 99%

Mitigating IoT Privacy-Revealing Features by Time Series Data Transformation

Wang,

Tang,

Fang

2023

JCP

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As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations.

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“…A less stringent technique is data deidentification, which is the removal or manipulation of direct and indirect identifiers such that reestablishing a link between a subject and his/her data would require a key that can be used to reverse the de-identification process (Garfinkel, 2015). Deidentification can be achieved by replacing identifiers with pseudonyms or codes, or pseudonymization (Ren et al, 2021). For example, Kallio et al (2020) assigned pseudonymization codes to participants that were used to log in and provide ratings of IEQ.…”

Section: Privacy Concerns Related To Wearablesmentioning

confidence: 99%

A Review of the Use of Wearables in Indoor Environmental Quality Studies and an Evaluation of Data Accessibility from a Wearable Device

Abboushi

Safranek

Bermudez

et al. 2022

Front. Built Environ.

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An understanding of indoor environmental quality (IEQ) and its effects on occupant well-being can inform building system design and operation. The use of wearables in field studies to collect subjective and objective health performance indicators (HPIs) from a large number of occupants could deliver important improvements in IEQ. To facilitate the use of wearables in IEQ studies, there is a need to identify which HPIs should be collected and to evaluate data accessibility from these devices. To address this issue, a literature review of previous IEQ studies was conducted to identify relationships between different IEQ factors and HPIs, with a focus on HPIs that were collected using wearables. A preliminary assessment of data accessibility from a selected wearable device (Fitbit Versa 2) was performed and documented. The review suggested the need to further investigate and collect sleep quality parameters, heart rate, stress response, as well as subjective ratings of comfort using wearables. The data accessibility assessment revealed issues related to missing data points and data resolution from the examined device. A set of recommendations is outlined to inform future studies.

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Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

Cited by 18 publications

References 43 publications

Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Mitigating IoT Privacy-Revealing Features by Time Series Data Transformation

A Review of the Use of Wearables in Indoor Environmental Quality Studies and an Evaluation of Data Accessibility from a Wearable Device

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