What Would Grandma Say? How to Respond When Cyber Hackers Reveal Private Information to the Public
Bibliographic record
Abstract
102 Cornell L. Rev. 1743 (2017)On August 18, 2015, a group of hackers named Impact Team released 37 million records—9.7 gigabytes of data—from the Toronto-based website Ashley Madison. The hackers claimed to be motivated by the alleged unscrupulous practices of Ashley Madison’s parent company, Avid Life Media Inc., such as false advertising and failing to follow through on a datapurging procedure for which it charged members a nineteendollar fee, The data dump has affected people from all walks of life, including 15,000 government employees, Vice President Joe Biden’s son, and individuals who, because of this data breach, learned that strangers used their e-mail addresses to create Ashley Madison accounts. Former subscribers to the website have received demands to pay money in the form of bitcoins as a ransom on their personal information. The data breach is likely related to at least two suicides thus far. Lawsuits against Avid Life Media Inc. have commenced, and more are in the planning stages; some plaintiffs hope to coordinate class action litigation. Despite the understandable outrage that the website’s members and former members feel, many lawyers are not optimistic about the chances of recovering damages from Ashley Madison or Avid Life Media Inc. This Note will explore the avenues for recovery available to individuals who lose control of their personal information when the security of an organization that collects or holds such information is compromised. This Note will begin by tracing the development of privacy jurisprudence as it specifically relates to the creation and eventual prominence of the Internet in the United States. Next, the piece will discuss the emerging split of authority surrounding a question of statutory interpretation presented by the Good Samaritan exception of the Telecommunications Act of 1996. Specifically, the issue is whether 47 U.S.C. § 230(e)(2)—a carve-out within the Good Samaritan exception that withholds immunity for civil liability for intellectual property claims—applies to federal intellectual property laws only, or to both federal and state intellectual property laws. The piece will then conclude that if the Supreme Court were to resolve this split of authority, it should, and likely would, hold that § 230(e)(2) withholds immunity from claims brought under both federal and state intellectual property laws. Finally, this Note will present new policy proposals that Congress and interactive computer service providers (ICSPs) could pursue in light of that conclusion.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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".