Towards Responsible Data Analytics: A Process Approach

The big data movement has been characterised by highly enthusiastic promotion, and caution has been in short supply. New data analytic techniques are beginning to be applied to the operational activities of government agencies and corporations. If projects are conducted in much the same carefree manner as research experiments, they will inevitably have negative impacts on the organisations conducting them, and on their employees, other organisations and other individuals. The limited literature on process management for data analytics has not yet got to grips with the risks involved. This paper presents an adapted business process model that embeds quality assurance, and enables organisations to filter out irresponsible applications

Big Data: Destroyer of Informed Consent

The \u27Revised Common Rule\u27 took effect on January 21, 2019, marking the first change since 2005 to the federal regulation that governs human subjects research conducted with federal support or in federally supported institutions. The Common Rule had required informed consent before researchers could collect and use identifiable personal health information. While informed consent is far from perfect, it is and was the gold standard for data collection and use policies; the standard in the old Common Rule served an important function as the exemplar for data collection in other contexts. Unfortunately, true informed consent seems incompatible with modern analytics and \u27Big Data\u27. Modern analytics hold out the promise of finding unexpected correlations in data; it follows that neither the researcher nor the subject may know what the data collected will be used to discover. In such cases, traditional informed consent in which the researcher fully and carefully explains study goals to subjects is inherently impossible. In response, the Revised Common Rule introduces a new, and less onerous, form of broad consent in which human subjects agree to as varied forms of data use and re-use as researchers\u27 lawyers can squeeze into a consent form. Broad consent paves the way for using identifiable personal health information in modern analytics. But these gains for users of modern analytics come with side-effects, not least a substantial lowering of the aspirational ceiling for other types of information collection, such as in commercial genomic testing. Continuing improvements in data science also cause a related problem, in that data thought by experimenters to have been de-identified (and thus subject to more relaxed rules about use and re-use) sometimes proves to be re-identifiable after all. The Revised Common Rule fails to take due account of real re-identification risks, especially when DNA is collected. In particular, the Revised Common Rule contemplates storage and re-use of so-called de-identified biospecimens even though these contain DNA that might be re-identifiable with current or foreseeable technology. Defenders of these aspects of the Revised Common Rule argue that \u27data saves lives.\u27 But even if that claim is as applicable as its proponents assert, the effects of the Revised Common Rule will not be limited to publicly funded health sciences, and its effects will be harmful elsewhere

Blockchain’s roles in strengthening cybersecurity and protecting privacy

This paper evaluates blockchain's roles in strengthening cybersecurity and protecting privacy. Since most of the data is currently stored in cloud data centers, it also compares how blockchain performs vis-vis the cloud in various aspects of security and privacy. Key underlying mechanisms related to the blockchain's impacts on the Internet of Things (IoT) security are also covered. From the security and privacy considerations, it highlights how blockchain-based solutions could possibly be, in many aspects, superior to the current IoT ecosystem, which mainly relies on centralized cloud servers through service providers. Using practical applications and real-world examples, the paper argues that blockchain's decentralized feature is likely to result in a low susceptibility to manipulation and forgery by malicious participants. Special consideration is also given to how blockchain-based identity and access management systems can address some of the key challenges associated with IoT security. The paper provides a detailed analysis and description of blockchain's roles in tracking the sources of insecurity in supply chains related to IoT devices. The paper also delves into how blockchain can make it possible to contain an IoT security breach in a targeted way after it is discovered. It discusses and evaluates initiatives of organizations, inter-organizational networks and industries on this front. A number of policy implications are discussed. First, in order to strengthen IoT, regulators can make it obligatory for firms to deploy blockchain in supply chain, especially in systems that are mission critical, and have substantial national security and economic benefits. Second, public policy efforts directed at protecting privacy using blockchain should focus on providing training to key stakeholders and increasing investment in this technology. Third, one way to enrich the blockchain ecosystem would be to turn attention to public–private partnerships. Finally, national governments should provide legal clarity and more information for parties to engage in smart contracts that are enforceable

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance

MADNESS OF THE CROWD - HOW BIG DATA CREATES EMOTIONAL MARKETS AND WHAT CAN BE DONE TO CONTROL BEHAVIOURAL RISK

In the recent years the term Big Data has been vividly discussed in management, the IS community and in the IT departments. Du to its potential for corporate performance and competitive advantage it has gained large attention up into the C-level-management. Observations on the possible negative consequnces of living in a data-driven world have mostly been limited to the perspective of an individual. For instance, concerns about data privacy have been vividly discussed when the growing hunger of governmental or private institutions for ever more and more personalized data was made public. This article starts with a critical reflection on the phenomena of Big Data, focusing on the consequnces for organizations and decision making. Next a case from the field of risk management is investigated in more detail using behavioural economics. Upon a series of experiments this paper sheds light on the possibility to create emotional markets using Big Data analytics in an un-reflected way. As a key takeaway this article should raise the awareness of behavioural risk. The presented work suggests extending the organizational risk framework by addressing behavioural risk

Open Data and Big Data Programs in Local Government Policy Analysis

This paper examines the development of policy around the open data programs for local government. Through a literature review, a survey of large municipalities in Ontario, and in-depth interviews, the research attempts to identify if there are factors that ensure whether the policy development process is more likely to be implemented along with the program or if there is a lack of policy development as a result of it. The findings reveal a definite lack of policy development with the open data program, which is likely due to the challenge for policy makers to ensure appropriate access and privacy protection, as technology makes information more accessible, and there are also emerging social issues that result from different generational expectations and values