scholarly journals PurExt: Automated Extraction of the Purpose-Aware Rule from the Natural Language Privacy Policy in IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lu Yang ◽  
Xingshu Chen ◽  
Yonggang Luo ◽  
Xiao Lan ◽  
Li Chen
Keyword(s):  
Data Collection ◽  
Natural Language ◽  
Recall Rate ◽  
Semantic Role Labeling ◽  
Privacy Policies ◽  
Novel Approach ◽  
High Recall Rate ◽  
Iot Devices ◽  
Machine Readable ◽  
The Internet Of Things

The extensive data collection performed by the Internet of Things (IoT) devices can put users at risk of data leakage. Consequently, IoT vendors are legally obliged to provide privacy policies to declare the scope and purpose of the data collection. However, complex and lengthy privacy policies are unfriendly to users, and the lack of a machine-readable format makes it difficult to check policy compliance automatically. To solve these problems, we first put forward a purpose-aware rule to formalize the purpose-driven data collection or use statement. Then, a novel approach to identify the rule from natural language privacy policies is proposed. To address the issue of diversity of purpose expression, we present the concepts of explicit and implicit purpose, which enable using the syntactic and semantic analyses to extract purposes in different sentences. Finally, the domain adaption method is applied to the semantic role labeling (SRL) model to improve the efficiency of purpose extraction. The experiments that are conducted on the manually annotated dataset demonstrate that this approach can extract purpose-aware rules from the privacy policies with a high recall rate of 91%. The implicit purpose extraction of the adapted model significantly improves the F1-score by 11%.

scholarly journals Design a blockchain-based middleware layer in the Internet of Things Architecture

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Tanweer Alam
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Smart Devices ◽  
The Internet ◽  
Novel Approach ◽  
Generation Computing ◽  
Critical Issues ◽  
Iot Devices ◽  
The Internet Of Things

In next-generation computing, the role of cloud, internet and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store big data in the cloud. The fog computing acts as an interface between cloud and IoT. The extension of fog in this framework works on physical things under IoT. The IoT devices are called fog nodes, they can have accessed anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing a new blockchains based middleware framework in the architecture of the Internet of Things is one of the critical issues of wireless networking where resolving such an issue would result in constant growth in the use and popularity of IoT. The proposed research creates a framework for providing the middleware framework in the internet of smart devices network for the internet of things using blockchains technology. Our main contribution links a new study that integrates blockchains to the Internet of things and provides communication security to the internet of smart devices.

scholarly journals Towards the Internet of Agents: An Analysis of the Internet of Things from the Intelligence and Autonomy Perspective

2018 ◽  
Vol 38 (1) ◽  
pp. 121-129 ◽  
Author(s):  
Pablo Antonio Pico Valencia ◽  
Juan A. Holgado-Terriza ◽  
Deiver Herrera-Sánchez ◽  
José Luis Sampietro
Keyword(s):  
Internet Of Things ◽  
Point Of View ◽  
The Internet ◽  
Iot Applications ◽  
Novel Approach ◽  
Iot Platforms ◽  
Iot Devices ◽  
Smart Industry ◽  
The Internet Of Things ◽  
Internet Of Agents

Recently, the scientific community has demonstrated a special interest in the process related to the integration of the agent-oriented technology with Internet of Things (IoT) platforms. Then, it arises a novel approach named Internet of Agents (IoA) as an alternative to add an intelligence and autonomy component for IoT devices and networks. This paper presents an analysis of the main benefits derived from the use of the IoA approach, based on a practical point of view regarding the necessities that humans demand in their daily life and work, which can be solved by IoT networks modeled as IoA infrastructures. It has been presented 24 study cases of the IoA approach at different domains ––smart industry, smart city and smart health wellbeing–– in order to define the scope of these proposals in terms of intelligence and autonomy in contrast to their corresponding generic IoT applications.

scholarly journals Research on Data Mining of Permission-Induced Risk for Android IoT Devices

2019 ◽  
Vol 9 (2) ◽  
pp. 277 ◽  
Author(s):  
Rajesh Kumar ◽  
Xiaosong Zhang ◽  
Riaz Khan ◽  
Abubakar Sharif
Keyword(s):  
Data Exchange ◽  
Malware Detection ◽  
The Internet ◽  
Static And Dynamic Analysis ◽  
Android Apps ◽  
Novel Approach ◽  
Android Applications ◽  
Iot Devices ◽  
Android Permission ◽  
The Internet Of Things

With the growing era of the Internet of Things (IoT), more and more devices are connecting with the Internet using android applications to provide various services. The IoT devices are used for sensing, controlling and monitoring of different processes. Most of IoT devices use Android applications for communication and data exchange. Therefore, a secure Android permission privileged mechanism is required to increase the security of apps. According to a recent study, a malicious Android application is developed almost every 10 s. To resist this serious malware campaign, we need effective malware detection approaches to identify malware applications effectively and efficiently. Most of the studies focused on detecting malware based on static and dynamic analysis of the applications. However, to analyse the risky permission at runtime is a challenging task. In this study, first, we proposed a novel approach to distinguish between malware and benign applications based on permission ranking, similarity-based permission feature selection, and association rule for permission mining. Secondly, the proposed methodology also includes the enhancement of the random forest algorithm to improve the accuracy for malware detection. The experimental outcomes demonstrate high proficiency of the accuracy for malware detection, which is pivotal for android apps aiming for secure data exchange between IoT devices.

scholarly journals Saving bandwidth and energy of mobile and IoT devices with link predictions

2020 ◽  
Author(s):  
Gabriel Orsini ◽  
Wolf Posdorfer ◽  
Winfried Lamersdorf
Keyword(s):  
Mobile Devices ◽  
Use Cases ◽  
Sensor Data ◽  
Software Infrastructure ◽  
Distributed Decision Making ◽  
Mobile Middleware ◽  
Novel Approach ◽  
Context Data ◽  
Iot Devices ◽  
The Internet Of Things

Abstract Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, service quality is often compromised. Aimed to address this issue, this paper proposes a novel approach to forecast the connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile operating systems, such as Android. We evaluated our approach with real sensor data and showed how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making on the mobile edge of the network.

scholarly journals A Taxonomy for the Representation of Privacy and Data Control Signals

2020 ◽  
Author(s):  
Kartik Chawla ◽  
Joris Hulstijn
Keyword(s):  
Natural Language ◽  
Service Provider ◽  
Personal Data ◽  
Privacy Policies ◽  
Natural Language Text ◽  
Control Signals ◽  
Data Control ◽  
Design Cycle ◽  
Machine Readable ◽  
The Relationship

In interacting with digital apps and services, users create digital identities and generate massive amounts of associated personal data. The relationship between the user and the service provider in such cases is, inter alia, a principal-agent relationship governed by a ‘contract’. This contract is provided mostly in natural language text, however, and remains opaque to users. The need of the hour is multi-faceted documentation represented in machine-readable, natural language and graphical formats, to enable tools such as smart contracts and privacy assistants which could assist users in negotiating and monitoring agreements. In this paper, we develop a Taxonomy for the Representation of Privacy and Data Control Signals. We focus on ‘signals’ because they play a crucial role in communicating how a service provider distinguishes itself in a market. We follow the methodology for developing taxonomies proposed by Nickerson et al. We start with a grounded analysis of the documentation of four smartphone-based fitness activity trackers, and compare these to insights from literature. We present the results of the first two iterations of the design cycle. Validation shows that the Taxonomy answers (10/14) relevant questions from Perera et al.’s requirements for the knowledge-modelling of privacy policies fully, (2/14) partially, and fails to answer (2/14). It also covers signals not identified by the checklist. We also validate the Taxonomy by applying it to extracts from documentation, and argue that it shows potential for the annotation and evaluation of privacy policies as well.

scholarly journals Improving Readability of Online Privacy Policies through DOOP: A Domain Ontology for Online Privacy

Digital ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 198-215
Author(s):  
Dhiren A. Audich ◽  
Rozita Dara ◽  
Blair Nonnecke
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Ontology ◽  
Online Privacy ◽  
Privacy Policy ◽  
Proof Of Concept ◽  
Privacy Policies ◽  
Privacy Concerns ◽  
Novel Approach

Privacy policies play an important part in informing users about their privacy concerns by operating as memorandums of understanding (MOUs) between them and online services providers. Research suggests that these policies are infrequently read because they are often lengthy, written in jargon, and incomplete, making them difficult for most users to understand. Users are more likely to read short excerpts of privacy policies if they pertain directly to their concern. In this paper, a novel approach and a proof-of-concept tool are proposed that reduces the amount of privacy policy text a user has to read. It does so using a domain ontology and natural language processing (NLP) to identify key areas of the policies that users should read to address their concerns and take appropriate action. Using the ontology to locate key parts of privacy policies, average reading times were substantially reduced from 29 to 32 min to 45 s.

Agricultural Application of Web of Things Architecture

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 61-63 ◽  
Author(s):  
Akihiro Fujii
Keyword(s):  
Internet Of Things ◽  
Agricultural Sector ◽  
Electronic Device ◽  
Specific Work ◽  
Science And Engineering ◽  
Unique Identifier ◽  
Agricultural Application ◽  
Iot Devices ◽  
The Internet Of Things ◽  
Type Pressure

The Internet of Things (IoT) is a term that describes a system of computing devices, digital machines, objects, animals or people that are interrelated. Each of the interrelated 'things' are given a unique identifier and the ability to transfer data over a network that does not require human-to-human or human-to-computer interaction. Examples of IoT in practice include a human with a heart monitor implant, an animal with a biochip transponder (an electronic device inserted under the skin that gives the animal a unique identification number) and a car that has built-in sensors which can alert the driver about any problems, such as when the type pressure is low. The concept of a network of devices was established as early as 1982, although the term 'Internet of Things' was almost certainly first coined by Kevin Ashton in 1999. Since then, IoT devices have become ubiquitous, certainly in some parts of the world. Although there have been significant developments in the technology associated with IoT, the concept is far from being fully realised. Indeed, the potential for the reach of IoT extends to areas which some would find surprising. Researchers at the Faculty of Science and Engineering, Hosei University in Japan, are exploring using IoT in the agricultural sector, with some specific work on the production of melons. For the advancement of IoT in agriculture, difficult and important issues are implementation of subtle activities into computers procedure. The researchers challenges are going on.

scholarly journals A novel approach for IoT tasks offloading in edge-cloud environments

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jaber Almutairi ◽  
Mohammad Aldossary
Keyword(s):  
Service Time ◽  
Edge Computing ◽  
Cloud System ◽  
Novel Approach ◽  
Delay Sensitivity ◽  
Cloud Environments ◽  
Application Characteristics ◽  
Iot Devices ◽  
Computational Resources ◽  
And Storage

AbstractRecently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

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