Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping Study

Abstract

The Internet of Things (IoT) is becoming a pervasive technology entangled in everyday life. The objective of IoT is to provide ubiquitous access to numerous devices and machines on service providers. However, IoT-based devices may expose a user to various privacy and security threats. Privacypreservation techniques focus on securing any type of data transfer between different parties. We aim to deliver the current state of the art in terms of privacy-preservation techniques used for IoT devices that have been discussed in the literature. Therefore, we carried out a systematic identification of the privacy-preservation techniques for IoT devices that have been described in the cybersecurity domain.To do so, we followed rigorous guidelines to define our research protocol to increase the repeatability and reliability of our results. A set of ten research questions was created to drive the analysis of our study. This research work comprehensively analyzes and discusses the privacy-preservation techniques for IoT devices published in five different academic venues . We identified 260 studies, mostly published between 2017 and 2021, that were systematically selected from an initial set of 1394 papers. The most active research areas in privacy-preservation techniques for IoT devices discuss cryptography techniques to improve the authentication process to access IoT devices. The majority of authors presented privacypreservation techniques for IoT that involved inventory privacy threats and discussed privacy interference attacks. We comprehensively analyze and discuss the trends, gaps, and possible future research directions of the privacy-preservation techniques used for IoT devices published in different academic venues.

A Systematic Review on Privacy-Aware IoT Personal Data Stores

“…Torre et al [18] performed a systematic mapping study to explore the role of privacypreservation (PP) techniques for IoT devices. Their findings comprise (i) the existing PP techniques and supporting tools, (ii) the goals and the IoT layers and devices covered by the studies, (iii) the most commonly discussed privacy threats and attacks, and (iv) the often-used evaluation metrics in the area.…”

“…Year Systematic Domain Main Goal Ogonji et al [15] ✓ IoT Privacy and security threats, attack surface, vulnerabilities, and countermeasures in IoT. Sarwar et al [25] x IoT and Fog Privacy preservation in IoT and fog-enabled IoT Zainuddin et al [16] x IoT Applications Privacy threats in IoT applications Islam et al [17] ✓ IoT and Big Data Types of privacy attacks and solutions in the IoT and Big Data contexts Zarandi et al [20] x Smart Farming Big Data privacy in smart farming Hireche et al [21] x IoMT Privacy and security in IoMT Shahid et al [23] x IoHT Data breaches and legal regulations in IoHT Alabdulatif et al [24] x IoNT Overview of IoNT and its security and privacy issues Torre et al [18] ✓ IoT Devices Privacy-preservation techniques for IoT devices Tokas et al [26] x IoT Privacy-enhancing technologies and GDPR-specific privacy principles Demertzi et al [27] x IIoT Privacy requirements in the Industrial IoT Fallatah et al [28] x PDS PDS and its advantages and disadvantages Zubaydi et al [29] ✓ IoT and Blockchain Blockchain solutions for IoT security and privacy issues Asqah and Moulahi [30] x…”

“…Many previous studies have reviewed privacy problems in the IoT context, focusing on different perspectives. A number of these studies have aimed to provide an understanding of the problem in general IoT applications [15][16][17][18][19]. In contrast, others have concentrated on specific IoT environments such as smart farming [20], the Internet of Medical Things [21,22], the Internet of Healthcare Things [23], and the Internet of Industrial Things [24].…”

Privacy Preserving Techniques and Their Applications in Elearning

“…Преглед на разработени подходи за защита на чувствителни данни при използване на устройства за Интернет на нещата е направен от Torre и др. [11]. Подходите са категоризирани в осем групи: техники за анонимизиране (включително k-анонимност, l-разнообразие, t-близост), за объркване, машинно обучение на няколко нива, децентрализирано машинно обучение, хомоморфно криптиране, създаване на модели на поток от данни с различни нива на поверителност, създаване на сбита версия на данните и създаване на сигурни индивидуални хранилища на данни.…”