Identity Crime: The Need for an Appropriate Government Strategy
Bibliographic record
Abstract
1. Introduction This paper investigates essential components for a robust strategy to combat in cyberspace for governments. term 'cyberspace' was coined by William Gibson in his 1984 novel Neuromancer. Our scope defines cyberspace the Internet; an online or digital world including more recent inter-connective innovations like mobile devices such phones and personal digital assistants. Cyberspace is a channel enabler and facilitator of crime. It is liked by perpetrators due to the anonymity of the transaction process. scope of ID fraud is not just limited to cyberspace; it includes offline creation of identities. Identity is a serious problem in the world today costing individuals, organisations and governments millions of dollars in losses, and expenditure on prevention, control, detection, and prosecution. Set out below is a summary of a number of studies that have attempted to determine costs (amounts in billions in stated currency). * United States of America (US): According to the Federal Trade Commission, theft cost American consumers US$5 and businesses US$48 last year, in (llett 2006) versus surveyed losses in the United States of America of US$56.6 in 2005 and falling to US$51 in 2006 and $45 in 2007 measured by Javelin Strategy & Research (2006, 4; 2007, 5; 2008, 2); * United Kingdom (UK): The reported annual cost of fraud has reached -1.72 billion up from [pounds sterling]1.3 in 2004 (UK Home Office 2006); * Canada: Annual cost was Canadian $2.5 to Canadian consumers and businesses, and the total annual cost to the Canadian economy was estimated at Canadian $5 billion (Brown and Kourakos 2003); * Australia: cost of fraud in Australia was estimated to be $1.1 a year with an estimation error of Australian $130 million in 2001-2002 (Cuganesan and Lacey 2003); and the * Global cost of in all its forms: US$221 by the end of 2003 (Aberdeen Advisory Group, May 2003) and estimate high US$2 trillion by December 2005? (The Fraud Advisory Panel 2003). With organisations being targeted by fraud perpetrators and incurring huge losses we also argue for implementation of an enterprise model. 'Identity' has three main attributes--biometric (physiological and behavioural characteristics), attributed, and biographical. In transacting with other entities in cyberspace we often use personal identifying information, such as, passwords, key tokens or personal identification numbers (PINs). Personal identifying information (PII) permits the authenticity or identifiability of an individual being verified. Verification is based on a prior exchange of details (identity) and questions plus their answers (unique to the individual) between parties. Identity attributes and personal identifying information within the scope of this paper are both critical information (documentation or data) enablers and facilitators of crimes. Identity crime, refers to offences in which a perpetrator uses a false in order to facilitate the commission of a crime (Australasian Centre for Policing Research 2006). Identity fraud, refers to the gaining of money, goods, services or other benefits through the use of a false identity (Australasian Centre for Policing Research 2006). Identity fraud events are preceded by theft and deception acts, where another's 'identity' is used and the perpetrator seeks anonymity to commit a crime. Identity theft is generally defined as the misappropriation of the (such the name, date of birth, current address or previous addresses) of another person, without their knowledge or consent. These attributes (proof of identity) are then used to obtain goods and services in that person's name (Credit Industry Fraud Avoidance System (CIFAS) 2007). …
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".