Trust and Confidence in a Secure Internet: A Public Good?
Bibliographic record
Abstract
Today we have near-universal availability of the Internet, with some 3.6 billion users in 200 countries worldwide. Simultaneously the intelligent mobile phone, with over 7 billion subscriptions globally, has become the most widely used communications device in the world, the access device of choice in developing countries and is often the only available device there for accessing the Internet and its associated services. In this paper the authors will show that the building of an appropriate institutional and legal infrastructure for the global digital marketplace and its underlying Internet infrastructure, as well as the creation of commonly accepted, understandable and internationally enforceable marketplace rules which provide trust and confidence for all those who operate in that marketplace or are affected by it, is a necessary condition for the efficient functioning of a global, digital economy. Although the Internet has transformed our economy and society, it was never designed and built for global, ubiquitous and secure commercial use. For the Internet to reach its full potential as a trusted global platform for commercial use, it must retain the trust and confidence of its users.Thus the creation of such Trust and Confidence in the Internet may be seen as analogous to creating a public good. The positive socio-economic benefits derived from the Internet and the World Wide Web are enormous and have largely been appropriated. However, the negative developments (e.g. malware, hacking, identity theft, the dark web and organized cyber-fraud and cyber-crime) are increasing in magnitude and cost.They need to be studied more carefully and the trade-offs with the benefits understood better, so that practical and workable remedial measures can be proposed, both nationally and globally. This paper makes the case that the status quo is untenable in the medium term. The increasing quantitative load put on the Internet by billions of vulnerable new users (e.g. mobile users) and new uses (e.g. the Internet of Things), combined with increasing net threats, will eventually degrade the public Internet unless new institutional and governance arrangements can be created. The task of building an environment of trust and confidence in the digital economy is complex: there is no magic or silver bullet. It will require a multi-stakeholder global approach and will involve concerted actions among many stakeholders: to create the requisite legal and regulatory environment; to develop voluntary codes of practice; to educate businesses, consumers and public service providers; and to create tools that are easy to use. Drawing upon the lessons of history and historical analogies, as well as examining some ideas proposed in various fora such as the OECD, ITU, IGF, GCIG and by various experts, we shall explore some scenarios to strengthen the Internet that could lead to more security for all users, and review what sorts of “rules of the road” will be necessary to achieve that outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".