Privacy in the Internet of Things: threats and challenges

By Jan Henrik Ziegeldorf, Óscar García-Morchón, Klaus Wehrle. Secur. Commun. Networks, 2014

555 citations recorded by the source corpus.

The Internet of Things paradigm envisions the pervasive interconnection and cooperation of smart things over the current and future Internet infrastructure. The Internet of Things is, thus, the evolution of the Internet to cover the real world, enabling many new services that will improve people's everyday lives, spawn new businesses, and make buildings, cities, and transport smarter. Smart things allow indeed for ubiquitous data collection or tracking, but these useful features are also examples of privacy threats that are already now limiting the success of the Internet of Things vision when not implemented correctly. These threats involve new challenges such as the pervasive privacy-aware management of personal data or methods to control or avoid ubiquitous tracking and profiling. This paper analyzes the privacy issues in the Internet of Things in detail. To this end, we first discuss the evolving features and trends in the Internet of Things with the goal of scrutinizing their privacy implications. Second, we classify and examine privacy threats in this new setting, pointing out the challenges that need to be overcome to ensure that the Internet of Things becomes a reality. Copyright © 2013 John Wiley & Sons, Ltd.

Subfields: Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/601e814e2e73a988e3737ec2ce4d5a215f511a9e

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Citations in the corpus

Transforming the Customer Experience through New Technologies (2020, background): “Ziegeldorf et al. (2014) have identified these privacy threats: identification of features unknown to the user, localization and tracking, profiling, making private information public (including photos and videos), inventory attack (collecting information about properties of owned things), and…”
Internet of Things and Big Data as enablers for business digitalization strategies (2020, background): “Profiling consumers can be useful for optimizing internal processes or analyzing consumers ’ interests, but the compilation of individual data and correlation with other profiles creates a swath of security concerns (Ziegeldorf et al., 2014).”
Machine Learning Based Intrusion Detection Systems for IoT Applications (2019, background): “Attackers may compromise the users’ security and privacy in order to gain access to their personal information, create monetary losses, and eavesdropping [72].”
Industrial Internet of Things: Challenges, Opportunities, and Directions (2018, background): “Generally speaking, privacy in IIoT is the threefold guarantee [112] for: 1) awareness of privacy risks imposed by things and services; 2) individual control over the collection and processing of information; and 3) awareness and control of subsequent use and dissemination to any outside entity.”
Toward Information Privacy for the Internet of Things: A Nonparametric Learning Approach (2018, background): “Achieving data privacy has been comprehensively addressed in [25], [38]–[41].”
Privacy and Security in Internet of Things and Wearable Devices (2015, background): “Few works have been published discussing the security of IoT devices themselves [14], [15].”
Equity Beyond Bias in Language Technologies for Education (2019, background): “NLP applications are mainstays in schools and have great reach, a trend poised to accelerate with the adoption of interactive, language-enabled devices like Alexa, both at home and in the classroom (Ziegeldorf et al., 2014; Horn, 2018; Boccella, 2019).”
IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot (2017, background): “Requirements for the agrifood authentication, access control within the IoT network, privacy and trust among users and things, and enforcement of security and privacy policies 13].”