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Record W2125974619 · doi:10.1109/cse.2009.387

FaceCloak: An Architecture for User Privacy on Social Networking Sites

2009· article· en· W2125974619 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
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceInternet privacyWorld Wide WebPopularityPersonally identifiable informationUsabilityArchitectureThe InternetEncryptionInformation privacyComputer securityHuman–computer interaction

Abstract

fetched live from OpenAlex

Social networking sites, such as MySpace, Facebook and Flickr, are gaining more and more popularity among Internet users. As users are enjoying this new style of networking, privacy concerns are also attracting increasing public attention due to reports about privacy breaches on social networking sites. We propose FaceCloak, an architecture that protects user privacy on a social networking site by shielding a user's personal information from the site and from other users that were not explicitly authorized by the user. At the same time, FaceCloak seamlessly maintains usability of the site's services. FaceCloak achieves these goals by providing fake information to the social networking site and by storing sensitive information in encrypted form on a separate server. We implemented our solution as a Firefox browser extension for the Facebook platform. Our experiments show that our solution successfully conceals a user's personal information, while allowing the user and her friends to explore Facebook pages and services as usual.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.065
GPT teacher head0.350
Teacher spread0.285 · 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

Quick stats

Citations179
Published2009
Admission routes1
Has abstractyes

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