Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms

Perera, Charith; McCormick, Ciaran; Bandara, Arosha K.; Price, Bob; Nuseibeh, Bashar

doi:10.1145/2991561.2991566

Cited by 93 publications

(90 citation statements)

References 32 publications

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“…While the previous three frameworks only deal with security threats, this framework focuses on privacy as opposed to the security focus of the other frameworks. The framework was named in a paper that attempted to create a privacy analysis framework for IoT as not properly applicable [7]. However, LINDDUN has been applied in the context of smart grids [8].…”

Section: An Overview Of Threat Modeling Frameworkmentioning

confidence: 99%

The IoT security gap: a look down into the valley between threat models and their implementation

Aufner¹

2019

Int. J. Inf. Secur.

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We claim to have identified gaps between threat modeling frameworks, threat model use in IoT security research and attacks that may be missed by current research. While security research includes sections known as 'threat models', these models are not supported by the categorization and standardization that threat modeling frameworks would have to offer. Then again, if existing threat modeling frameworks were used, they would still allow many vulnerabilities to pass through undetected, since they are meant for software-only projects. This work will explain the origins of IoT research, enumerate common threat modeling frameworks and give an insight into the state of IoT security research. In the course of this, it will become clear how these gaps came to be and what research directions would help to close them.

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Section: An Overview Of Threat Modeling Frameworkmentioning

confidence: 99%

The IoT security gap: a look down into the valley between threat models and their implementation

Aufner¹

2019

Int. J. Inf. Secur.

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“…Several recent researches have been conducted for the privacy-preserving for the IoT based services [28][29][30][31][32][33][34][35][36]. In order to handle the massive amount of data, the most convincing solution is the federation of the IoT and cloud computing.…”

Section: Privacy-preserving For Iotmentioning

confidence: 99%

“…Jin, et al, presented a framework for the realization of smart cities through the Internet of Things (IoT), which encompasses the complete urban information system and forms a transformational part of the existing cyber-physical system [33]. The authors in [34] proposed a privacy-by-design (PbD) framework that can guide software engineers to systematically assess the privacy capabilities of IoT applications and middleware platforms, thus the proposed PbD framework can also be used to design new IoT platforms.…”

Section: Privacy-preserving For Iotmentioning

confidence: 99%

Efficient location privacy algorithm for Internet of Things (IoT) services and applications

Sun

Chang

Ramachandran

et al. 2017

Journal of Network and Computer Applications

141

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Location-based Services (LBS) have become a very important area for research with the rapid development of Internet of Things (IoT) technology and the ubiquitous use of smartphones and social networks in our daily lives. Although users can enjoy a lot of flexibility and conveniences from the LBS with IoT, they may also lose their privacy. Untrusted or malicious LBS servers with all users' information can track users in various ways or release personal data to third parties. In this work, we first analyze the current dummy-location selection (DLS) algorithm-an efficient location privacy preservation approach and design an attack algorithm for DLS (ADLS) for test emerging IoT security. For efficiently preserving user's location privacy, we propose a novel dummy location privacy-preserving (DLP) algorithm by considering both computational costs and various privacy requirements of different users. Extensive simulation experiments have been carried out to evaluate the efficiency of the proposed schemes. Evaluation results show that the ADLS algorithm has a high probability of identifying the user's real location out from chosen dummy locations in the DLS algorithm. Our proposed DLP algorithm has clear advantages over the DLS algorithm in term of lower probability of revealing the user's real location and improved computational cost and efficiency (i.e., time, speed, accuracy, and complexity) while preserve the same privacy level as DLS algorithm.

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“…These are general PbD frameworks that ; do not consider the characteristics of mHealth technologies. Closer to the mHealth domain are examples of PbD frameworks that focus on the Internet of Things (IoT), which have been used to evaluate the current privacy capabilities of a system with regards to data collection and processing [20], however these provide few insights into privacy usability.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach (Preprint)

Hutton¹,

Price²,

Kelly³

et al. 2017

Preprint

Self Cite

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Background: The increasing sophistication of self-tracking technologies allows individuals to generate significant quantities of timely and accurate data about their lifestyle, biology, and environment, which can be used to support health interventions and measure outcomes. However, such user-generated data is often not treated with the sensitivity of other medical data, with data stored and processed by vendors with commercial motivations, such as enabling targeted advertising and making inferences from linked data. As the sensors and applications which enable these technologies continue to become more sophisticated, the privacy implications may become more severe. Methods for systematically identifying and

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Privacy-by-Design Framework for Assessing Internet of Things Applications and Platforms

Cited by 93 publications

References 32 publications

The IoT security gap: a look down into the valley between threat models and their implementation

The IoT security gap: a look down into the valley between threat models and their implementation

Efficient location privacy algorithm for Internet of Things (IoT) services and applications

Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach (Preprint)

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