Big Data, Open Data, Privacy Regulations, Intellectual Property and Competition Law in an Internet-of-Things World: The Issue of Accessing Data

Author(s):  
Björn Lundqvist
2017 ◽  
pp. 491-506
Author(s):  
Padmalaya Nayak

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.


2018 ◽  
Vol 20 (32) ◽  
Author(s):  
Maria Salles Coelho Mello Franco

 Resumo: O presente trabalho analisa até que ponto o big data e open data invadem a privacidade de usuários da internet. Dessa maneira, será organizado da seguinte forma: inicialmente o assunto será contextualizado, trazendo uma breve descrição sobre que é big data e open data. Em seguida, abordaremos os possíveis problemas que o mecanismos podem gerar à privacidade dos usuários de internet. Posteriormente, benefícios trazidos por esses instrumentos tecnológicos serão analisados. Em seguida, demonstraremos qual vem sendo a resposta jurídica para o contexto e, por fim, as considerações finais. Palavras Chave: Big data. Open data. Privacidade. Internet. Abstract: This paper analyzes the extent to which big data and open date invade the privacy of Internet users. In this way, it will be organized as follows: initially it will be contextualized, bringing a brief description of what is big data and open date. Then we discuss the possible problems that the mechanisms can generate the privacy of Internet users. Later, benefits brought by these technological tools will be analyzed. Then we demonstrate what has been the legal response to the context and finally, the final considerations .  Keywords : Big data. Open data. Privacy. Internet. 


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

Author(s):  
Padmalaya Nayak

Internet of Things (IoT) is not a futuristic intuition, it is present everywhere. It is with devices, Sensors, Clouds, Big data, and data with business. It is the combination of traditional embedded systems combined with small wireless micro sensors, control systems with automation, and others that makes a huge infrastructure. The integration of wireless communication, micro electro mechanical devices, and Internet has led to the development of new things in the Internet. It is a network of network objects that can be accessed through the Internet and every object can be identified by unique identifier. By replacing IPV4, IPV6 plays a key role and provides a huge increase of address spaces for the development of things in the Internet. The objective of IoT application is to make the things smart without the human intervention. With the increasing number of smart nodes and amount of data that generated by each node is expected to create new concerns about data privacy, data scalability, data security, data manageability and many more issues that have been discussed in this chapter.


Author(s):  
Xiang Wu ◽  
Yongting Zhang ◽  
Aming Wang ◽  
Minyu Shi ◽  
Huanhuan Wang ◽  
...  

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.


2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
A April

Abstract Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such Open Data can shed light on the causes of disease and effects of treatment including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual’s characteristics, known as personalized or precision medicine. Precision medicine treatments will take personalization to the next level and be effective for individual patients based on their genomic, environmental, and lifestyle factors. High-throughput sequencing technologies and open databases have made precision medicine possible but up to now only large research centres could handle the large scale technology needed for its processing. Aside from issues such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, recent advances by Berkeley using open source Big Data technologies and Cloud Computing Services has allowed precision medicine studies to be conducted by small and agile research labs and researchers around the world.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lei Zhang ◽  
Yu Huo ◽  
Qiang Ge ◽  
Yuxiang Ma ◽  
Qiqi Liu ◽  
...  

Various applications of the Internet of Things assisted by deep learning such as autonomous driving and smart furniture have gradually penetrated people’s social life. These applications not only provide people with great convenience but also promote the progress and development of society. However, how to ensure that the important personal privacy information in the big data of the Internet of Things will not be leaked when it is stored and shared on the cloud is a challenging issue. The main challenges include (1) the changes in access rights caused by the flow of manufacturers or company personnel while sharing and (2) the lack of limitation on time and frequency. We propose a data privacy protection scheme based on time and decryption frequency limitation that can be applied in the Internet of Things. Legitimate users can obtain the original data, while users without a homomorphic encryption key can perform operation training on the homomorphic ciphertext. On the one hand, this scheme does not affect the training of the neural network model, on the other hand, it improves the confidentiality of data. Besides that, this scheme introduces a secure two-party agreement to improve security while generating keys. While revoking, each attribute is specified for the validity period in advance. Once the validity period expires, the attribute will be revoked. By using storage lists and setting tokens to limit the number of user accesses, it effectively solves the problem of data leakage that may be caused by multiple accesses in a long time. The theoretical analysis demonstrates that the proposed scheme can not only ensure safety but also improve efficiency.


Author(s):  
Muzzammil Hussain ◽  
Neha Kaliya

Data privacy is now-a-days a special issue in era of Internet of Things because of the big data stored and transmitted by the public/private devices. Different types and levels of privacy can be provided at different layers of IoT architecture, also different mechanisms operate at different layers of IoT architecture. This article presents the work being done towards the design of a generic framework to integrate these privacy preserving mechanisms at different layers of IoT architecture and can ensure privacy preservation in a heterogeneous IoT environment. The data is classified into different levels of secrecy and appropriate rules and mechanisms are applied to ensure this privacy. The proposed framework is implemented and evaluated for its performance with security and execution time or primary parameters. Various scenarios are also evaluated, and a comparison is done with an existing mechanism ABE (Attribute Based Encryption). It has been found that the proposed work takes less time and is more secure due to short key length and randomness of the parameters used in encryption algorithm.


Author(s):  
Kung-Chung Liu ◽  
Shufeng Zheng

This chapter discusses the protection of relevant data and issues that might block access to such data. There are at least three kinds of data, data specifically generated for the purpose of AI, big data, and copyright-protected data. Data specifically generated for AI should qualify as works worthy of copyright protection as compilations. If such data are public-sector information, measures that may facilitate the widest re-uses of public data should be taken. If such data are from the private sector, attention needs to be paid to whether these data are of critical importance and whether the use of competition law and/or ex ante regulation will be needed to ensure access. The issue of protection of big data should not be addressed in the sense of giving proprietary entitlement over big data to any entity. When looking at the private-sector components of big data, there are legal, technical and market factors working against the formation, accumulation, and free flow of big data. When looking at the public-sector components of big data, both the importance of open data and the re-use of public sector information come to the fore. Concerning the issue of accessing big data, there are barriers to its collection, storage, synthesis, analysis, and usage. However, behavioural barriers warrant special scrutiny. In that regard, competition law as an ex post remedy can be relied upon but is of limited use; therefore sector-specific regulation might be needed. As for copyright-protected data, the real issue is more about access than protection.


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