Privacy Enhancing Technologies in the Internet of Things: Perspectives and Challenges

Cha, Shi-Cho; Hsu, Tzu-Yang; Xiang, Yang; Yeh, Kuo-Hui

doi:10.1109/jiot.2018.2878658

Cited by 72 publications

(69 citation statements)

References 154 publications

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“…Authors of [37] applied a multi-objective algorithm to hide sensitive information in order to achieve privacy preserving data mining (PPDM). In IoT devices, data may be collected from people without their consent, hence posing privacy risk [38]. However, authors in [39] proposed a user-friendly framework to achieve consents in Bluetooth low-energy IoT devices.…”

Section: Review Of Existing Privacy Preserving Modelsmentioning

confidence: 99%

“…The framework is seen to have improved the privacy policy agreement in IoT appliances. Also, a survey of privacy enhancing technologies (PETs) to mitigate privacy risk, privacy legislation, and their challenges in IoT is found in [38]. Authors in [40] gave an overview of approaches to safeguard privacy in the industrial internet of things (IIoT), and emphasized a secure algorithm for endpoints.…”

Section: Review Of Existing Privacy Preserving Modelsmentioning

confidence: 99%

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A Neuron Noise-Injection Technique for Privacy Preserving Deep Neural Networks

Adesuyi

Kim

2020

Open Computer Science

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AbstractData is the key to information mining that unveils hidden knowledge. The ability to revealed knowledge relies on the extractable features of a dataset and likewise the depth of the mining model. Conversely, several of these datasets embed sensitive information that can engender privacy violation and are subsequently used to build deep neural network (DNN) models. Recent approaches to enact privacy and protect data sensitivity in DNN models does decline accuracy, thus, giving rise to significant accuracy disparity between a non-private DNN and a privacy preserving DNN model. This accuracy gap is due to the enormous uncalculated noise flooding and the inability to quantify the right level of noise required to perturb distinct neurons in the DNN model, hence, a dent in accuracy. Consequently, this has hindered the use of privacy protected DNN models in real life applications. In this paper, we present a neuron noise-injection technique based on layer-wise buffered contribution ratio forwarding and ϵ-differential privacy technique to preserve privacy in a DNN model. We adapt a layer-wise relevance propagation technique to compute contribution ratio for each neuron in our network at the pre-training phase. Based on the proportion of each neuron’s contribution ratio, we generate a noise-tuple via the Laplace mechanism, and this helps to eliminate unwanted noise flooding. The noise-tuple is subsequently injected into the training network through its neurons to preserve privacy of the training dataset in a differentially private manner. Hence, each neuron receives right proportion of noise as estimated via contribution ratio, and as a result, unquantifiable noise that drops accuracy of privacy preserving DNN models is avoided. Extensive experiments were conducted based on three real-world datasets and their results show that our approach was able to narrow down the existing accuracy gap to a close proximity, as well outperforms the state-of-the-art approaches in this context.

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Section: Review Of Existing Privacy Preserving Modelsmentioning

confidence: 99%

Section: Review Of Existing Privacy Preserving Modelsmentioning

confidence: 99%

A Neuron Noise-Injection Technique for Privacy Preserving Deep Neural Networks

Adesuyi

Kim

2020

Open Computer Science

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“…The Internet of Things (IoT) [ 1 , 2 ] is an emerging concept that changes the interactions of people with things in everyday life. IoT [ 3 ] allows the connection of ubiquitous objects/things to the Internet in order to provide innovative services that can lead to time- and money-saving in the daily activity of the individuals and that can increase the quality of life [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Software Architecture for the Industrial Internet of Things—A Conceptual Model

Ungurean

Gaitan

2020

Sensors

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The Internet of Things (IoT) is an emerging concept that has revolutionized the use of new technologies in everyday life. The economic impact of IoT becoming very important, and it began to be used in the industrial environment under the name of the Industrial Internet of Things (IIoT) concept, which is a sub-domain of IoT. The IIoT changes the way industrial processes are controlled and monitored, increasing operating efficiency. This article proposes a software architecture for IIoT that has a low degree of abstraction compared to the reference architectures presented in the literature. The architecture is organized on four-layer and it integrates the latest concepts related to fog and edge computing. These concepts are activated through the use of fog/edge/gateway nodes, where the processing of data acquired from things is performed and it is the place where things interact with each other in the virtual environment. The main contributions of this paper are the proposal and description of a complete IIoT software architecture, the use of a unified address space, and the use of the computing platform based on SoC (System on Chip) with specialized co-processors in order to be able to execute in real-time certain time-critical operations specific to the industrial environment.

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“…In keeping with this trend, today, IoT data markets can be categorized into two major approaches regarding privacy [11]: i) privacy protection market and ii) privacy valuation market. In the privacy protection market, privacy-enhancing technologies are provided to data providers, which minimize potential privacy infringement risks and protect possible privacy violations caused by IoT services/applications [12], [13]. On the other hand, in the privacy valuation market, data brokers offer proper benefits or incentives to data providers (who consider the value between privacy protection and valuation) for collecting IoT datasets under agreement or consent.…”

Section: Introductionmentioning

confidence: 99%

Competitive Data Trading Model With Privacy Valuation for Multiple Stakeholders in IoT Data Markets

Park

Lee

et al. 2020

IEEE Internet Things J.

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With the widespread of Internet of Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since datadriven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this paper proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their dataset to the data service provider as a non-cooperative game model. Based on the Nash Equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.

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Privacy Enhancing Technologies in the Internet of Things: Perspectives and Challenges

Cited by 72 publications

References 154 publications

A Neuron Noise-Injection Technique for Privacy Preserving Deep Neural Networks

A Neuron Noise-Injection Technique for Privacy Preserving Deep Neural Networks

A Software Architecture for the Industrial Internet of Things—A Conceptual Model

Competitive Data Trading Model With Privacy Valuation for Multiple Stakeholders in IoT Data Markets

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