Security

Author(s):  
Ronald M. Baecker

Throughout history, humanity has invented valuable technologies and ways to organize society. These innovations are typically accompanied by risks. Fire cooks food, and also provides heat on cold nights. Yet, when left unchecked, fire can cause huge damage as well as loss of life. Cities enabled new forms of community and commerce. However, they brought us more thievery, and made it easier for epidemics to spread. The automobile allowed a separation of locales for work and residence; trucks allowed goods to be shipped long distances. But vehicular accidents have caused far greater injury and loss of life than did mishaps with horses and mules. Information technology, like other technologies, has potential for good and for harm. In the first six chapters, we introduced aspects of human activity, such as education, medicine, and government, in which IT has been transformative and mostly positive. The next three chapters examine areas in which the negatives of IT are dominant, in which risks seem everywhere. This chapter focuses on security. IT security flaws are exploited by outsiders for personal or political gain. In Chapter 8 we shall look at safety, where the risks are often injury or loss of life. In Chapter 9, we shall look at privacy, where the risks are exposure of private, confidential, and even sensitive information. Security is the attribute of a computer system that ensures that it can continue to function properly after an attack. Attacks against computer systems happen routinely now, are in the news almost every week, and are accelerating in numbers and in impact. Damage to both individuals and organizations—financial losses, chaos, and deteriorating morale— is severe. We shall provide a primer on the multitude of ways computer systems, from large networks to mobile phones, can be ‘hacked’ so that they no longer function properly. We shall define the most common kinds of destructive software, often called malware. We will discuss large-scale data breaches, which now happen frequently and expose the personal data of millions to billions of people. The word hackers refers to individuals who disrupt digital technologies and thereby damage the functioning of an institution or a society.

2019 ◽  
Vol 15 (4) ◽  
pp. 365-378 ◽  
Author(s):  
Reinder Broekstra ◽  
Judith Aris-Meijer ◽  
Els Maeckelberghe ◽  
Ronald Stolk ◽  
Sabine Otten

Exponential increases in digital data and calls for participation in human research raise questions about when and why individuals voluntarily provide personal data. We conducted 36 in-depth interviews with ex-participants, participants, and nonparticipants in a biobank to identify key factors influencing trust in centralized large-scale data repository for human research. Our findings indicated that trust depends strongly on whether such data repository benefits the public, the interests of data collectors, the characteristics of the collected data, and application of informed consent for retaining control over personal data. Concerns about the aims and range of data repository appeared to influence withdrawal of participation. Our findings underscore ethical and practical issues relating to data collection and consent procedures in human research.


Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012884
Author(s):  
Hugo Vrenken ◽  
Mark Jenkinson ◽  
Dzung Pham ◽  
Charles R.G. Guttmann ◽  
Deborah Pareto ◽  
...  

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS.


2021 ◽  
Vol 4 (1) ◽  
pp. 3-14
Author(s):  
Zdzislaw Polkowski ◽  
◽  
Sambit Kumar Mishra ◽  

In a general scenario, the approaches linked to the innovation of large-scaled data seem ordinary; the informational measures of such aspects can differ based on the applications as these are associated with different attributes that may support high data volumes high data quality. Accordingly, the challenges can be identified with an assurance of high-level protection and data transformation with enhanced operation quality. Based on large-scale data applications in different virtual servers, it is clear that the information can be measured by enlisting the sources linked to sensors networked and provisioned by the analysts. Therefore, it is very much essential to track the relevance and issues with enormous information. While aiming towards knowledge extraction, applying large-scaled data may involve the analytical aspects to predict future events. Accordingly, the soft computing approach can be implemented in such cases to carry out the analysis. During the analysis of large-scale data, it is essential to abide by the rules associated with security measures because preserving sensitive information is the biggest challenge while dealing with large-scale data. As high risk is observed in such data analysis, security measures can be enhanced by having provisioned with authentication and authorization. Indeed, the major obstacles linked to the techniques while analyzing the data are prohibited during security and scalability. The integral methods towards application on data possess a better impact on scalability. It is observed that the faster scaling factor of data on the processor embeds some processing elements to the system. Therefore, it is required to address the challenges linked to processors correlating with process visualization and scalability.


Author(s):  
Jack Hile

The increasing prevalence of large-scale data breaches prompted Australia to strengthen the Privacy Act by enacting the Privacy Amendment (Notifiable Data Breaches) Act to regulate the behaviour of entities entrusted with personal data. However, this paper argues that these legislative instruments are ineffective when dealing with data breaches and their associated problems. In supporting this conclusion, this paper first develops a criterion for effective data breach law, and then evaluates the Australian framework against this criterion to determine its operational effectiveness. In addition, this paper analyses practical developments in the area of data-breach law to garner insights as to how the Australian framework can be made more effective. Ultimately, this paper concludes that the Australian framework is ineffective when dealing with large-scale data breaches, and recommends future legislative amendment as a means of bolstering its effectiveness.


2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang

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