Guide Networks of Innovation: Change and Meaning in the Age of the Internet

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Write a customer review. Showing of 1 reviews. Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. Format: Hardcover Verified Purchase. A short book, written from the perspective of a research interested in the links between "Technology" and "Society". To me the book was worth reading if only for its focus on two insights. A new product, a new technology, that does not provoke changes of behaviour will end up just being a foot note in some academic record.

Socially irrelevant. Specialized organizations produce incremental innovations. Radical innovations require a more open environment. The history of the development of Internet, covered in the book, is a good example. A quick read, non academical. One person found this helpful. See the review. Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more about Amazon Giveaway. Set up a giveaway. There's a problem loading this menu right now. Furthermore, the article examines the complexity of tackling technological and legislative challenges in protecting individual privacy.

It concludes by summarizing these issues in terms of the future implications of the IoT and the loss of privacy. Across most domains, societal functioning has become increasingly dependent on information and communication technology, as well as the management of massive data streaming through physical and virtual environments.

Big data has emerged as an area of significant interest in research and applications for organizations dealing with or anticipating an overwhelming flow of data. Individual privacy regarding big data has especially taken hold as a central issue affecting different technology areas as connectivity and information sharing have far outpaced data protection efforts Perera et al.

Widely publicized breaches of large databases exposed significant and escalating threats to individual privacy and control over personal data.

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In , a security breach of an American health insurance company, Anthem, led to the theft of personal information of more than 78 million customers Mathews, The information included names, dates of birth, social security numbers, and income data, all of which were likely sold in underground markets. The total number of affected individuals and the sensitive nature of large data breaches are alarming; they also point to an urgent need to the convergence of technology, legislation, user policies, and awareness in protecting privacy.

Big data and individual privacy protection are further complicated by the evolution of networks of networks, also referred to as the Internet of Things IoT. This new paradigm promises to enable existing and future devices to be connected to local and virtual networks and, eventually, communicate autonomously with these networks and other devices for functions such as gathering and analyzing data Borgohain et al.

For instance, new applications are enabling users to check the status of their home appliances from their smartphones, monitor private property, and synchronize their devices while increasing the likelihood of exposing the large amount of data collected and stored in these devices and networks to other individuals and entities. According to Russo and colleagues , by , there will be over billion sensor devices that are interconnected. These sensors will be found in home electronic systems, health monitoring equipment, cars, and smartphones.

As the surface area of data expands exponentially through the IoT, the implications of individual privacy threats of this pervasive interconnectivity are immense. Current breaches of large databases and their impact provide insights into how the future of big data and the IoT is shaped.

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This article examines the implications of compromised individual privacy in the age of the IOT as it relates to big data. Next, it examines the extent to which recent big data breaches have exposed the vulnerability of personal data. The examples illustrate the different pathways and impact of individual data loss. Then, the article places issues and challenges of data privacy loss into the context of the age of the IoT, and it emphasizes the fundamental complexity of the IoT and the how it is likely to present further technological, legislative, and user experience challenges to protecting individual privacy.

Finally, the article integrates and summarizes the previous sections by examining opportunities in security and individual privacy protection in the age of the IoT. The underlying assumption of the article is that the collection of data from IoT devices and customization based on the collected data create vulnerabilities in individual data privacy. As a framework to guide the discussion, Figure 1 provides an overview of individual privacy when big data is examined in the age of IoT.

The roles of technological and legislative solutions in protecting individual data privacy continue to change and evolve. Big data, as a concept, has been around for two decades since being used by Cox and Ellsworth While initially referring to extensive volumes of scientific data, big data has since been defined in a number of ways. V olume reflects the tremendous amounts of data created from a number of sources and across different platforms such as mobile devices and applications and smart grids, as well as social media such as Facebook.

The sheer volume of big data is likely to increase substantially as IoT-enabled technology will continue to be designed to generate data from multiple devices and sources.

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Variety refers to the nature of data generated. For instance, structured data from geographic information systems as well as unstructured data from websites are found in numerous formats. Velocity reflects the speed with which data is not only generated from a myriad of sources, but the frequency of data capture, analysis, and the application of information in decision making. Thus, value refers to the actual use of the data collected. Physical devices or sensors may not, by themselves, provide data that can be used for predictive modelling in medicine or retail, for instance.

However, multiple devices and sensors can provide data that, when aggregated, provides valuable information upon analysis. Big data, therefore, is likely about the above four attributes and their scaling to ever greater numbers of devices, infrastructures, and networks. At its core, big data describes the wide availability of data in digital form, with a concomitant presence of data mining and knowledge-generation capability across numerous networks. The collection and storage of large volumes of data has held the promise of data-driven discovery in diverse fields including scientific research, healthcare, industry, manufacturing and education Chen et al.

Massive volumes coupled with wider availability aimed to fulfill this promise through the development of data exploration and mining technologies. The purpose of data mining, therefore, is to uncover useful and novel information from data stored in large databases, thereby being predictive or descriptive. This is an especially important development in fields reliant upon large data for making those predictions to be generalized across populations such as medicine and commerce.

The data mining process, in general, involves several major steps whereby data is cleaned, transformed, and mined for information. Big data and the use of machine learning algorithms have become inextricably linked with data mining recently. A main reason is that datasets have grown larger and more complex, and traditional learning methods of managing such volumes while extracting useful data have fallen short.


Furthermore, while the volume of data has increased, its quality has remained inconsistent; data mining efforts face low quality, multi-form data across numerous applications and systems, and are further complicated by the lack of effective security solutions to share such data. As noted by Shukla :. Especially when the Internet of Things becomes a reality in improving the lives of people, improving quality of automation systems, and improving transportation system performance, machine learning and data mining will be ready to deliver technologies, algorithms, and possibly products that can be directly used to make those systems perform in the most optimal fashion, adapting to changing situations, and securing the system against hackers who would certainly want to disrupt such systems or try to breach privacy of people who will be connected to such networks.

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Data mining for effective decision making may seem innocuous from the perspective of private data exposure. For instance, census data collected may aggregate ages to arrive at descriptive statistics for age groups, but will be expected to not provide access individual identifying information such as names and addresses. However, these expectations are outside the control of individuals whose data may be stored, transferred, shared, and analyzed by different individuals and organizations.

As both data volume and data mining interest increase in the IoT paradigm, the issue of privacy becomes more urgent. From a review of recent literature, it is apparent that the IoT encompasses an understanding of how networks of networks will connect devices, infrastructure, and systems, among others, through a new Internet.

Perera and colleagues. A comprehensive definition of the IoT is also presented by Russo and colleagues , who state that:. The IoT promises unprecedented advancements across knowledge-based industries and fields. According to a review of literature on the IoT Russo et al. On a wider, societal scale, IoT applications are numerous and wide-ranging given that they are used in commercial, environmental, and critical infrastructure settings Chen et al.

It is expected that, with an increased capability in analyzing large data, high-quality information will guide such functions as monitoring air quality and pollution indices, as well as monitoring food as it is transported across the globe. The agricultural industry can exploit in-ground sensors and irrigation-control software to automate its soil management, while reducing costs associated with inclement conditions Russo et al.

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Commercial applications have noted the ever-increasing role of supply chain management and logistics, both of which are made more efficient and cost-effective when connected devices are programmed to provide basic decision-making capability. In summary, the IoT will allow billions of objects, such as mobile devices, and virtual environments to exchange data. With machine learning, devices and environments may exchange such data autonomously while extracting meaningful data. However, the IoT — by definition — is complex and covers extensive data landscapes, structures, and contexts. The IoT is a developing target for interconnectivity of devices and environments in a network of networks.

The potential entry points and vulnerabilities to data privacy breaches are also developing, and a key question is whether security measures can be concomitantly interoperable and scalable. However, breaches of large datasets are a reality, and recent years have shown how vulnerable individual data is to loss of control, theft, and exploitation. The loss of personal information to unauthorized and illegal means did not start with the Internet; individuals were likely to lose their financial information such as credit card statements or social insurance numbers from thieves rummaging through personal effects or property.

The widespread digitization of everyday living, from financial transactions to personal communication, to business dealings, however, has exposed individual information to unauthorized access to entities from across the globe Bekara, In the process, it has prompted a revisiting of privacy threats and an examination of individual privacy and control of data generated by our activities as a right deserving of user and legal protections.

It remains that the right to the massive data collected currently through databases — which are expected to be interconnected, sometimes autonomously, through the IoT — has legal frameworks and privacy-enhancing technologies but they are lagging to provide adequate protections Han et al. Some examples of big data collection may seem mundane. As more devices are enabled to provide similar information, we observe that cars also provide data on location, while household efficiency and security protection are connected to handheld devices.

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  • Taken together, the information from disparate devices provide extensive information on individual and behavioural patterns, which is a privacy concern Schroeck et al. This situation is similar to the collection of browsing history and purchasing behaviour used to tailor online activities to an individual. However, they are also similar in exposing individuals to the loss of their information. In healthcare, for instance, health information collection is now enabled in many everyday devices such as iPhones or FitBits, providing continuous data collection of key health behaviour, a function reserved in the past through medical intervention to a limited number of people Suciu et al.

    Abinaya, Kumar, and Swathika examined the application of the IoT in devising an information system based on the ontology method. The researchers explored a system that aimed to connect emergency medical services with hospital-based services. The implications of this data collection and storage, and the ability to provide real-time analysis and provision to healthcare providers, represent a revolutionary advancement in health monitoring and preventive care Abinaya et al.

    Once again, however, privacy risks are inherent in the collection, storage, and exchange of this data. Individuals may lose control of who views their information, which has the potential to result in exposure of health conditions and practices, but may also have ramifications for employment and health insurance Borgohain et al.