The FTC, the Unfairness Doctrine, and Privacy by Design: New Legal Frontiers in Cybersecurity
Bibliographic record
Abstract
more than a year later, in August 2013, the Commission brought an administrative action against LabMD, Inc., a small, little-known medical testing company in Atlanta, Georgia, alleging violations of the Act in connection with alleged security breaches in 2008 and 2012.3 Although the FTC complaints in the two actions appeared similar, following a format used consistently by the FTC in data breach actions, 4 the two cases were remarkably different.Perhaps most importantly, Wyndham is a Fortune 500 company which brought in upwards of $5 billion in revenue in 2015, and owns more than 55 hospitality brands, whereas LabMD had approximately 20 employees at the time it was sued.5 The breaches which inspired the FTC to sue, as well as the alleged security failures, were quite different in the two cases as well.The case against Wyndham centered on three different breaches over a period of two years, compromising "more than 619,000 payment card account numbers," which were posted to a domain registered in Russia.6 The breaches, according to the FTC, cost consumers "more than $10.6 million in fraud loss." 7 Wyndham's alleged failings were specific and egregious: according to the FTC, the company failed to use adequate firewalls, allowed payment card information to be stored in readable text, did not employ common user ID and password procedures, and two separate times failed to correct errors after major breaches.8 By contrast, although the FTC alleged broad failures by LabMD to implement and maintain a comprehensive security program, the company's purported wrongdoing centered on the decision of a single employee to install P2P file sharing software on a company computer.9 A file containing the personal information of LabMD customers was leaked through the 1. 15 U.S.C. § § 41-58 (2012).Pardau PP v3 (Do Not Delete) 4/6/2017 12:20 PM
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.071 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".