Fog-based local and remote policy enforcement for preserving data privacy in the Internet of Things

2019 ◽  
Vol 7 ◽  
pp. 100069 ◽  
Author(s):  
Abduljaleel Al-Hasnawi ◽  
Steven M. Carr ◽  
Ajay Gupta
2020 ◽  
Vol 17 (5) ◽  
pp. 2388-2395
Author(s):  
M. Vivek Anand ◽  
S. Vijayalakshmi

IoT is changing the way for a world, where many of our daily objects will be connected with each other and will interact with their environment in order to collect information and automate certain tasks. IoT requires seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Protecting data in the internet of things is essential for making the IoT environment secure. In order to secure the data on the internet of things, the blockchain will provide distributed peer to peer networks. Blockchain-based internet of things is making a secure environment in the IoT environment. Data are stored in the form of images in IoT devices that are captured in various locations in the IoT environment for processing. Images are stored as data in the blockchain and it acts as a transaction. This paper expresses the environment of blockchain-based internet of things with image validation. This paper will explain this domain with an example of a criminal’s image identification with image processing techniques to provide better service to the cyber intelligence agency to find criminals easily. The identification of criminals is done by comparing the images of the criminals’ identification.


Subject IoT ecosystem. Significance The market for the Internet of Things (IoT) or connected devices is expanding rapidly, with no manufacturer currently forecast to dominate the supply chain. This has fragmented the emerging IoT ecosystem, triggering questions about interoperability and cybersecurity of IoT devices. Impacts Firms in manufacturing, transportation and logistics and utilities are expected to see the highest IoT spending in coming years. The pace of IoT adoption is inextricably linked to that of related technologies such as 5G, artificial intelligence and cloud computing. Data privacy and security will be the greatest constraint to IoT adoption.


2015 ◽  
Vol 713-715 ◽  
pp. 2462-2466
Author(s):  
Xiu Rong Li ◽  
Shuang Zheng ◽  
Ya Li Liu

In this paper, we describe some privacy threats in the Internet of Things and some research works on privacy protection. We present a new scheme base on cryptosystem to protect privacy in the Internet of Things. The scheme includes location privacy protection, data privacy homomorphism mechanism and information hiding technology, and secure multi-party computation on data privacy.


Author(s):  
Muneer Bani Yassein ◽  
Wail Mardini ◽  
Amnah Al-Abdi

Internet of Things (IoT) is one of the most active and hot topics these days in which most of our everyday objects are connected with each other over internal and external networks. As in any data communication paradigm there are security aspects that should be taken care of. The traditional security mechanisms are usually not applicable in IoT because there are different standards involved, this make the security preservation is one of the main challenges in IoT. According to previous surveys, there are many of security issues in regards to IoT. In this chapter, five issues from the security issues in IoT are discussed; Access Control, Authentication, Privacy, Policy Enforcement, and Trust. After that, major proposed solutions from the literature is listed and compared according to the strength and weakness points for each of them.


2013 ◽  
Vol 411-414 ◽  
pp. 141-144
Author(s):  
Jun Zhou ◽  
Zhen Yu Yang

The Internet of things is widespread concerned by the whole society now. As an important component of the Internet of things, wireless sensor network has wide application prospect in various fields such as medical and health, military defense. The traditional data privacy protection technology of PKI system used in the WSN networks has its own weakness. This paper presents the secret key sharing mechanism to protect data privacy. The secret key, remote node and base station used to communicate, was divided into multiple secrets. The multiple secrets were distributed in the nodes which connect directly to the base station node. Only through collect more than threshold number of multi-secret that can decrypt the communication data between the base station and the remote node. To be safer, we used digital watermarking technology to protect the data transmission between the base station and the aggregate node. These techniques combined with the data slice, homomorphism encryption technology to protect data privacy, construct a safe and efficient wireless sensor networks.


2015 ◽  
Vol 17 (3) ◽  
pp. 32-39 ◽  
Author(s):  
Charith Perera ◽  
Rajiv Ranjan ◽  
Lizhe Wang ◽  
Samee U. Khan ◽  
Albert Y. Zomaya

2021 ◽  
Vol 8 (4) ◽  
pp. 685-733
Author(s):  
Jennifer Zwagerman

Technology advancements make life, work, and play easier and more enjoyable in many ways. Technology issues are also the cause of many headaches and dreams of living out the copier destruction scene from the movie “Office Space.” Whether it be user error or technological error, one key technology issue on many minds right now is how all the data produced every second of every day, in hundreds of different ways, is used by those that collect it. How much data are we talking about here? In 2018, the tech company Domo estimated that by 2020 “1.7 MB of data will be created every second” for every single person on Earth. In 2019, Domo’s annual report noted that “Americans use 4,416,720 GB of internet data including 188,000,000 emails, 18,100,000 texts and 4,497,420 Google searches every single minute.” And this was before the pandemic of 2020, which saw reliance on remote technology and the internet skyrocket. It is not just social media and working from home that generates data—the “Internet of Things” (“IoT”) is expanding exponentially. From our homes (smart appliances and thermostats), to entertainment (smart speakers and tablets), to what we wear (smartwatches and fitness devices), we are producing data constantly. Over 30 billion devices currently make up the IoT, and that number will double by 2025. The IoT is roughly defined as “devices—from simple sensors to smartphones and wearables—connected together.” That connection allows the devices to “talk” to each other across networks that stretch across the world, sharing information that in turn can be analyzed (alone or combined with data from other users) in ways that may be beneficial to the user or the broader economy. The key word in that last sentence is “may.” When it comes to the data that individuals and businesses across the world produce every second of every day, some of it—perhaps most of it—could be used in ways that are not beneficial to the user or the entire economy. Some data types can be used to cause harm in obvious ways, such as personal identifying information in cases of identity theft. While some data types may seem innocuous or harmful when viewed on their own, when combined with other data from the same user or even other users, it can be used in a wide variety of ways. While I find it beneficial to know how many steps I take in a day or how much time I sleep at night, I am not the only individual or entity with access to that information. The company that owns the device I wear also takes that information and uses it in ways that are beyond my control. Why would a company do that? In many instances, “[t]he data generated by the Internet of Things provides businesses with a wealth of information that—when properly collected, stored, and processed—gives businesses a depth of insight into user behavior never before seen.” Data security and privacy in general are issues that all companies manage as they work to protect the data we provide. Some types of data receive heightened protections, as discussed below, because they are viewed as personal, as private, or as potentially dangerous since unauthorized access to them could cause harm to the user/owner. Some states and countries have taken a step further, focusing not on industry-related data that needs particular types of protection, but in-stead looking at an individual’s overall right to privacy, particularly on the internet. Those protections are summarized below. It makes sense, you might say, to worry about financial or healthcare data remaining private and to not want every website you have ever visited to keep a file of information on you. But why might we care about the use of data in agricultural operations? Depending on who you ask, the answer may be that agricultural data needs no more care or concern than any other type of business data. Some argue that the use of “Big Data” in agriculture provides opportunities for smaller operations and shareholders. These opportunities include increased power in a market driven for many years by the mantra “bigger is better” and increased production of food staples across the world—both in a more environmentally-friendly fashion. While the benefits of technology and Big Data in the agricultural sector unarguably exist, questions remain as to how to best manage data privacy concerns in an industry where there is little specific law or regulation tied to collection, use, and ownership of this valuable agricultural production data. In the following pages, this Article discusses what types of data are currently being gathered in the agricultural sector and how some of that data can and is being used. In addition, it focuses on unique considerations tied to the use of agricultural data and why privacy concerns continue to increase for many producers. As the Article looks at potential solutions to privacy concerns, it summarizes privacy-related legislation that currently exists and ends by looking at whether any of the current privacy-related laws might be used or adapted within the agricultural sector to address potential misuse of agricultural data.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiayi Guo ◽  
Shah Nazir

With the constant developments in Internet communication and the rise of the Internet of Things (IoT), technologies incorporating intelligent manufacturing have given birth to the growing industry and production lines. The network of IoT is generally interconnected with different devices through the Internet. The interactions of the IoT devices form smooth and functional communication require the connectivity of billions of objects. The devices of IoT can preserve, capture, share, and analyze data with nodes connected to the world. Various issues of the IoT such as monitoring the data, stealing the data, privacy of the data, tracking of the data, and many other aspects of the data are becoming challenges for the modern-day industry. The role of computational intelligence in proper analysis, managing, and many different perspectives of the IoT is prominent. Such computational intelligence can solve real-time problems with low cost and time. The IoT has provided solutions for poor scalability, system integration, and difficulties in coordinated operation across the emerging systems. The influence of the proposed study is to offer a wide-ranging overview of the current literature related to the Internet of Things based on intelligent techniques in workable computing. The study has considered the search process in the most popular libraries and presented an analysis of the research work done so far. The analysis and results of the study support the progress in the field, which will help researchers come up with new solutions.


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