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Record W366855202

Securing Mobile Technology & Financial Transactions in the United States

2012· article· en· W366855202 on OpenAlexaboutno aff
Eleanor Lumsden

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessInternet privacyMobile phoneFinancial servicesWork (physics)Mobile technologyConsumer privacyComputer securityFinancial transactionInformation privacyFinanceTelecommunicationsMobile computingComputer scienceEngineeringDatabase transaction
DOInot available

Abstract

fetched live from OpenAlex

One of the paradoxes of modern life is the conflict between convenience and security. Advances in technology simultaneously usher in progress and pain. The development of mobile and smartphone technology will have a significant positive impact on financial transactions and the average consumer’s access to financial services. Nevertheless, there are several reasons to secure mobile technology and financial transactions in the United States. First, cell phones increase the risk to personal security and most U.S. wireless carriers are using outdated encryption technology. Second, many cell phone users are more concerned with convenience—caring more about the availability and functionality of smartphone applications—than with potential security threats. This will change as more consumers use their phones for financial transactions. Third, evidence from other developed countries, including Canada and several nations in Europe, has shown that it is possible to provide additional security protections for consumers. Americans rely on their smartphones to transmit financial data about themselves, their work places and families. While some privacy laws have been interpreted to cover the unique threats posed by mobile technology, most do not, and security and privacy issues in regards to mobile banking have been largely unheralded. This Article identifies existing privacy laws and security regulations that have been applied to mobile technologies by federal and state governments, by courts, and by various regulatory agencies. The Article then analyzes the shortcomings of the current regulatory framework in the United States. After examining several policy recommendations, as well as current standards in the telecommunications industry, the Article concludes with several suggestions for mitigating the risks posed by emerging mobile technology. Without entirely upending the current system, U.S. laws can be expanded and streamlined to address future challenges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0090.004
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.009
GPT teacher head0.274
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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