The issue is perhaps more important than the simplified view of a potential threat to a monopolistic network provider. It is about the best pathway to innovation. On one hand is the role of the RSP as a single‐side market; Layer 1 unbundling would effectively provide a RSP with a more flexible vehicle to reach end‐users and an input to modify and improve the LFC’s wholesale service catalogue upon which retail services are built. On the other hand it is the RSP’s role as a truly digital platform. As previously discussed, as a service provider a retailer is able to turn its market role from being one link in a conventional supply chain to becoming a digital platform its strategy is less reliant on managing internal resources, such as seeking unbundled Layer 1 services, and more dependent on creating and sustaining a thriving community. Managing the virtuous circle that positive cross‐network effects may generate becomes the platform’s main challenge.
76.5 Conclusion
This chapter postulates that New Zealand’s Ultra‐Fast Broadband network deployment under construction during the second decade of the 21st century, with its technical and regulatory decisions made thus far, offers the right kind of telecommunications infrastructure that will best ease up the road to efficient digital markets.
As a platform, fibre uptake has benefitted from policy decisions on the consumer side; in particular, government investment pays for a fibre connection at zero cost to any consumer (the household side) at any time during the first 10 years. The other side, the retailer side, has seen the arrival of close to 100 retail service providers nationwide that have started businesses on the UFB.
Policy and regulation are proving to be factors that shape the UFB broadband ecosystem; in the transition from copper to fibre in New Zealand, regulation will play a fundamental role in accelerating the opportunities for entrepreneurs to develop their fibre‐based services and the consumers to benefit from competition on the content market, that is, the market developed by retailers.
The platform model cannot only be observed on the UFB as retailers meet consumers (seeking access to fibre); it is a model that could be followed by leading retailers willing to exploit the synergies between content providers and consumers eager for content. As much as the most successful model of digital disruption is seen in the creation and development of digital platforms that have overturned conventional markets and created communities around them, new players in New Zealand’s digital ecosystem too could also realise the untapped potential that a technologically superior infrastructure offers and leverage it with innovative services that fully demand and use its most prominent features such as high speed, reliability and open access. Regulatory changes will have to be carefully introduced so that the nascent ecosystem is able to deliver quality, low prices and innovation.
Acknowledgements
the author wants to acknowledge partial funding from the University of Auckland Business School Dean’s AP fund.
References
1.
F. Beltrán, “Ultra-Fast Business; Rewiring New Zealnad Economy,” Auckland, University of Auckland Business Review, 2014, pp. 26–35.
2.
MBIE, “Broadband Deployment Update Q2-2016,” 06 2016. [Online]. Available: http://www.mbie.govt.nz/info-services/sectors-industries/technology-communications/fast-broadband/documents-image-library/june-2016-quarterly-broadband-deployment-update.pdf. [Accessed 15 08 2016].
3.
M. Gregory, “How much do FTTP NBN connections really cost?,” 18 09 2015. [Online]. Available: http://www.businessspectator.com.au/article/2015/9/18/technology/how-much-do-fttp-nbn-connections-really-cost. [Accessed 20 04 2016].
4.
OECD, Broadband Network and Open Access. OECD Digital Economy Papers, 2013, 2013.Crossref
5.
F. Beltrán, Using the economics of platforms to understand the broadband-based market formation in the New Zealand Ultra-Fast Broadband Network, 36 Hrsg., Bd. 9, Telecommunications Policy, 2012, pp. 724–735.Crossref
6.
G. Parker, S. P. Choudary and M. Van Alstyne, Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You, W. W. Norton & Company. Kindle Edition, 2016.
7.
MBIE, “Review of the Telecommunications Act 2001,” 2001. [Online]. Available: http://www.mbie.govt.nz/info-services/sectors-industries/technology-communications/communications/regulating-the-telecommunications-sector/review-of-the-telecommunications-act-2001.[Accessed 15 08 2016].
Further Reading
8.
M. Van der Wee, “The Efficiency and Effectiveness of a Mixed Public-Private Broadband Deployment: The Case of New Zealand’s Ultra-Fast Broadband Deployment,” 2016. [Online]. Available: http://ssrn.com/abstract=2732829 or http://dx.doi.org/10.2139/ssrn.2732829 [Accessed at 15 02 2016]. [Accessed 15 02 2016].
Part XVII
Active Cyber Defense
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_77
77. Securing the Opportunities of the Digitized Economy
Yuval Diskin1
(1)Cymotive Technologies Ltd., Herzlia, Germany
Yuval Diskin
Email: [email protected]
77.1 Strategic Partnering
Cyber Security is no longer only a technology challenge. The Cyber Security industry, which today is led by very talented technology experts, is suffering from a lack of Dynamic Defense strategies. These strategies exist only partially in some states in the government level, and are lacking in different industries and in the private sector. For this to happen, governments should encourage Cyber Security experts and strategists from the government and security systems, to migrate to the private sector and at the same time, to find ways to civilize some of the technologies used by the governments. Another good step would be to partner up with companies accountable for critical national infrastructure such as aerospace and defense. Both ways would establish a relationship that may be able to take actions against adversaries.
77.2 Intelligence Is Key
Dynamic Defense should become the new Cyber Security approach as soon as possible. This means that we need a comprehensive dynamic approach which involves both, External and Internal Intelligence. For a company it is crucial to maintain up‐to‐date intelligence by getting information about Cyber Security threats from own records, but also from third parties. Those external sources are fundamental as experts can give greater insight of the current state‐of‐the‐art, have prepared threat profiles, and reveal attack vectors, i. e. specific strategic paths hackers take to get inside a network. The wide variety of contextual information helps a company to get a deeper understanding of the threat and the potential approach an attacker is going to pursue together with corresponding countermeasures.
New knowledge about the behavior of an attacker should be extracted following the intelligence cycle in order to enable faster decision making. By defining the weak spots of their company’s network and estimating the technical capabilities of an attacker, a Cyber Security team can acquire knowledge about a known adversary’s typical tactics, techniques, and procedures (TTPs). After setting up sophisticated threat profiles, suitable approaches should be integrated into the system and upgraded over time, including both, an increase of internal sensors for monitoring and external resources. In order to make active defense be able to process the high amount of data emerging from log files and user records, a dramatic increase in automation is needed.
77.3 Always Awake Security Brain
Thus, cutting edge technology solutions and products are needed including a future generation of SIEM and SOC: AASB (“Always Awake Security B
rain”) involving Artificial Intelligence and Machine Learning capabilities will enable real time security decision‐making for fast and effective responses to new threats. Internal hunting teams are often used to actively search for and manage cyber‐attackers. Finding them is a hard task, as hackers have the advantage of first move and a wide array of tools and techniques to hide their entry and activities. For that matter, Cyber Security operators need to identify and analyze internal use patterns, access logs and scan applications to detect anomalies. In order to understand what constitutes to abnormal behavior, profiles of “normal” user behavior are created manually and refined using machine learning. It is crucial to perform vulnerability analysis for detecting the most dangerous intrusion strategies. Once found, strategies revolve around handling the intruder. While the reflexive approach suggests to expel the threat as fast as possible, a detected intruder can be analyzed for valuable information regarding his arsenal of tools, and of course his actions. A wise decision is typically to waste the attacker’s time by setting up honey pots, tar pits, and deceptive sand boxes.
77.4 Improved Research and Development
Aside all these we will always need best practices and professional governmental regulation of high Cyber Security standards for all sectors and industries. One of the first things that should be regulated in the era of Internet‐of‐Things, is the deep integration of Cyber Security experts in the research and development processes of software and hardware products. Companies need to be aware that conventional passive defense mechanisms, such as firewalls and intrusion detection systems, are by far not enough to compensate the broad bandwidth of attacks they might face against adversaries. Therefore, already in the very early conception stage of software and hardware, active measures need to be respected that aim at monitoring an attacker’s behavior. This will eventually help reducing slow response times to intruders and zero day exploits.
77.5 The Diversity of Threats
Cyber Security solutions and products should be developed not only for the Known‐Known Threats, but also for the Unknown‐Known Threats and even for the Unknown‐Unknown Threats. This approach already exists in the field of counter‐terrorism and should be adopted to the field of Cyber Security.
Most companies traditionally focus on threats from external actors, and therefore orienting the defense system against outer threats. However, insiders, especially personnel of the own company, are generally trusted while having access to valuable data. Because access privileges are often not managed appropriately, insiders are susceptible against methods such as social engineering which no intrusion detection system may prevent. It is unavoidable to implement a data loss protection (DLP) and change the mind‐set of internals to protect against internal threats.
Such insider threats may be subdivided into three categories. An errant insider might be unaware of current security protocols and may accidentally compromise important information of the network by forwarding an email with high confidentiality. Hijacked insiders are the most common type of breaches and involve stolen credentials, i. e. via key loggers, for example. Malevolent insiders, that is internals that compromise the network at will, mostly do so because of bribery or coercion. This depicts a great risk and can often be spotted with strict DLP rules. A chain of trust and structured concepts for access privilege need to be employed to keep up with those type of threats.
77.6 Conclusion
The digitized economy is developing very fast but Cyber Security is still far behind. The strategic challenges and threats folded in these tremendous opportunities are already here. The faster we move to the next generation of Cyber Security the more it will enable us to enjoy the fruits of the digitized economy and to live in a more secured world.
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_78
78. Black Market Value of Patient Data
Christina Czeschik1
(1)Serapion Beratung & Fachredaktion, Essen, Germany
Christina Czeschik
Email: [email protected]
78.1 Valuing Personal (Health) Data
Like every kind of personal data1, personal health data is inherently valuable. However, different stakeholders will value a set of health data in very different ways, some of which are: Health care providers require patient records and other kinds of patient data as the basis of professional diagnostic and therapeutic decision‐making.
For insurance companies, personal health data facilitates prediction of health risks of insured and not‐yet‐insured individuals, thus allowing them to adapt premiums or forego contracts altogether.
Universities, research organizations and pharma companies require patient data, to develop and test new diagnostic and therapeutic means, such as novel drugs.
Consumer‐oriented companies covet personal health information to optimize their product marketing, e. g., in the fields of wearable technologies or nutritional supplements.
For the patient, finally, the value of personal health data lies of course in the potential improvement of health that can be achieved when it is used by health professionals – but even more than the above‐mentioned parties, the patient is also concerned with keeping private information private.
Thus, one could say that while it is valuable for health care providers and companies in the private sector to have an individual’s personal health data, its value for patients lies both in having and in not sharing it – at least not involuntarily.
The OECD Report “Exploring the Economics of Personal Data” [1] has examined more formally the problem of attributing value to personal data. Here, methods of assigning a monetary value to personal data are divided into two broad classes: (1) those based on individuals’ valuation and (2) those based on market valuation.
If individuals’ valuation is to be the main aspect, the following measures may serve as a basis: Surveys and economic experiments
(i. e., monetary value of personal data as reported by probands in surveys and economic experiments)
Individual willingness to pay to protect data
(i. e., monetary amount individuals are willing to pay to keep their data private)
When considering market valuation, possible measures are: Market capitalization, revenues or net income per data record
(i. e., a company’s market capitalization, revenues or income divided by number of personal data records used by the company)
Market prices for data
(i. e., price for data record in data broker market)
Cost of a data breach
(i. e., cost incurred by a company or individual to recover from a data breach)
Data prices in illegal markets
According to the OECD, the latter may even be a more accurate measure of the value of data sets than other suggested measures because illegal data may represent a rival good, i. e., a good in which the value decreases as more customers gain access to it. Hence, an illegal market’s market‐clearing price may be closer to the good’s full market price. One of the disadvantages of valuing personal data by illegal market prices is, of course, the lack of market transparency. Also, prices tend to fluctuate with momentary ups and downs in supply and demand [2].
Data prices in illegal markets have been well‐investigated for years with regard to data sets that lend themselves more easily to financial exploitation than personal health data, such as credit card data [3]. The mere fact that personal health data as well is sold and bought in illegal markets, however, is shifting into focus only recently, with the increasing digitalization of processes and patient information in hospitals and practices [4] and the advent of ever more sophisticated (and surprisingly unsophisticated) malicious attacks on health care providers
’ IT systems [5].
Hence, the black market price of personal health data is relevant not only from a theoretical standpoint – as one of several measures to assess the monetary value of personal health data – but also and maybe more importantly to answer the question regarding what kind of profit attackers and data thieves targeting the health care system expect to gain from their activities. However, before tackling this issue, the next section of this chapter will discuss whether or not patient data are actually at risk for theft and misuse.
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