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Record W2150018653 · doi:10.1109/scc.2007.56

Enabling User Control with Personal Identity Management

2007· article· en· W2150018653 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsAthabasca UniversitySimon Fraser University
Fundersnot available
KeywordsIdentity managementIdentity (music)Computer scienceService (business)Computer securityControl (management)Broadcasting (networking)Identity theftInternet privacyAccess controlBusiness

Abstract

fetched live from OpenAlex

Being proactive and vigilant is the best defense against identity theft and the invasion of privacy. This recurrent advice from the public broadcasting attests that security breaches can happen and no identity management system can provide full-proof security. The challenge is even greater in service-oriented architectures where each user has their identities scattered across many services and has no control over management of those identities. Recent research in the area of the user-centric identity management makes user control and consent the key concept for identity management, but there is no consensus on the level of user-centricity. This paper proposes a service-oriented architecture framework called personal identity management that truly puts users in control over the management of their identities. The advantages of this proposal can be demonstrated through a comparison analysis of relevant identity management systems against a set of criteria required for today's identity management.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.228
Teacher spread0.220 · 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