Connecting Physical-World to Cyber-World: Security and Privacy Issues in Pervasive Sensing

Zhong, Hong; Huang, Xinyi; Yang, Panlong; Jin, Shi; Xie, Lihua; Wang, Kun

doi:10.1007/978-3-030-01150-5_3

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…In the context of the well-documented cybersecurity risk assessment field, there is a plethora of standardised frameworks and guidelines that aim to capture and document the cyber risks which are triggered as a result of vulnerabilities, attacks and technical flaws that emerge from the cyber field. In addition, there is a wide spectrum of works that focus on the analysis of cyber-physical systems, but they revolve around the cyber threats of those systems [56,57]. Hence, the connection between the two worlds, and the assessment of risks as a result of cascading actions, which are triggered from actors or events in the physical world and may have an impact to the cyber dimension, is a research domain with major challenges and room for innovation.…”

Section: Cybersecurity and Privacy Risk Assessment: The Road Aheadmentioning

confidence: 99%

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

et al. 2021

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As the upsurge of information and communication technologies has become the foundation of all modern application domains, fueled by the unprecedented amount of data being processed and exchanged, besides security concerns, there are also pressing privacy considerations that come into play. Compounding this issue, there is currently a documented gap between the cybersecurity and privacy risk assessment (RA) avenues, which are treated as distinct management processes and capitalise on rather rigid and make-like approaches. In this paper, we aim to combine the best of both worlds by proposing the APSIA (Automated Privacy and Security Impact Assessment) methodology, which stands for Automated Privacy and Security Impact Assessment. APSIA is powered by the use of interdependency graph models and data processing flows used to create a digital reflection of the cyber-physical environment of an organisation. Along with this model, we present a novel and extensible privacy risk scoring system for quantifying the privacy impact triggered by the identified vulnerabilities of the ICT infrastructure of an organisation. We provide a prototype implementation and demonstrate its applicability and efficacy through a specific case study in the context of a heavily regulated sector (i.e., assistive healthcare domain) where strict security and privacy considerations are not only expected but mandated so as to better showcase the beneficial characteristics of APSIA. Our approach can complement any existing security-based RA tool and provide the means to conduct an enhanced, dynamic and generic assessment as an integral part of an iterative and unified risk assessment process on-the-fly. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that such holistic security and privacy mechanisms can reach their full potential towards solving this conundrum.

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Section: Cybersecurity and Privacy Risk Assessment: The Road Aheadmentioning

confidence: 99%

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

et al. 2021

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“…Any pervasive mobile communication framework needs to take privacy and anonymity into account [98][99][100]. Pervasive sensing applications require the cooperation of strangers who will not trust each other [17] and, therefore, incentives to back such schemes are essential [101][102][103].…”

Section: Privacy and Anonymitymentioning

confidence: 99%

A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research

Carvalho

Sofia

2020

IoT

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Mobile sensing has been gaining ground due to the increasing capabilities of mobile and personal devices that are carried around by citizens, giving access to a large variety of data and services based on the way humans interact. Mobile sensing brings several advantages in terms of the richness of available data, particularly for human activity recognition. Nevertheless, the infrastructure required to support large-scale mobile sensing requires an interoperable design, which is still hard to achieve today. This review paper contributes to raising awareness of challenges faced today by mobile sensing platforms that perform learning and behavior inference with respect to human routines: how current solutions perform activity recognition, which classification models they consider, and which types of behavior inferences can be seamlessly provided. The paper provides a set of guidelines that contribute to a better functional design of mobile sensing infrastructures, keeping scalability as well as interoperability in mind.

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“…Mostly, in smart homes [8] and smart o ces, people may be irritated by wearing the device on them all the time. Tag-based localization is highly prone to the leakage of privacy and personal information, e.g., a person's daily routine [9][10][11][12]. Many techniques have been proposed for the localization of non-cooperative targets such as thermal infrared sensors, pressure sensors, sound source, light sensors, electric eld, ultrasonic sensors, and radio frequency-(RF-) based localization schemes [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Directional Sensors for Privacy-Ensured Device-Free Target Localization in Indoor Environment

Rehman

Ellahi²,

Iqbal

et al. 2019

Wireless Communications and Mobile Computing

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This paper presents two radio frequency (RF) sensors with different directivities designed and tested for device-free localization (DFL) in an indoor environment. Mostly, in smart homes and smart offices, peoples may be irritated by wearing the device on them all the time. As compared with device-based localization, the proposed sensors can localize both cooperative and non-cooperative targets (intruders and guests etc.) without privacy leakages. Both sensors are tested to detect the change in received signal strength (ΔRSS) due to the presence of an obstacle. RF sensors, i.e., antennas are designed to operate in the ISM band of 2.4–2.5 GHz. Experimental results show that the sensor with higher directivity provides better ΔRSS that helps in improved accuracy to detect a device-free target.

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Connecting Physical-World to Cyber-World: Security and Privacy Issues in Pervasive Sensing

Cited by 3 publications

References 27 publications

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research

Design and Implementation of Directional Sensors for Privacy-Ensured Device-Free Target Localization in Indoor Environment

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