To Observe and Protect? How Digital Rights Management Systems Threaten Privacy and What Policy Makers Should Do About it
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.
Bibliographic record
Abstract
The author begins the chapter by distinguishing between technological protection measures (TPMs) and digital rights managements (DRMs) systems, examining how such technologies are used to enforce corporate copyright policies and express copyright permissions imposed by a DRM through a registration process that requires purchasers to hand over personal information. Given DRM's extraordinary surveillance capabilities, the author argues that anti-circumvention laws must contain express provisions and penalties to protect citizens from organizations using TPMs and DRMs to pirate personal information, engage in excessive monitoring, and preclude people from exercising their right to access and control personal information. The author presents the view that any law which protects surveillance technologies used to enforce copyright must also protect people's privacy. Such laws must contain express provisions and penalties that protect citizens from organizations using TPMs and DRMs to engage in excessive monitoring or the piracy of personal information. In determining an appropriate balance, the author introduces three public policy considerations: (i) the Anonymity Principle; (ii) Individual Access; and (iii) Freedom From Contract. The author concludes with three corollary recommendations: (i) include an express provision prohibiting the circumvention of privacy by TPM/DRM, notwithstanding license provisions to the contrary; (ii) include an express provision stipulating that a DRM licence is voidable when it violates privacy law; and (iii) include an express provision permitting the circumvention of TPM/DRM for personal information protection purposes. These recommendations provide a set of counter-measures necessary to offset the new powers and protections afforded to TPM and DRM.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it