Edward Snowden disclosures turn the fears of surveillance into reality: the impact and transformation in information security
Bibliographic record
Abstract
More than two years passed since the biggest event on information security and privacy which is the disclosure of very sensitive documents on the National Security Agency in United States. Those disclosures had a lot of resonance and impact in term of discussions between people who supported the act and others who consider it as crime and breach of trust. The fact, is that the impact was huge not only regarding information technology but it has been extended to economy and politics. This paper provide a holistic view and analyse of the current situation of information technology security and privacy especially with the lack and limited researches that have been done to study the transformation in information security strategies, policies and law after Edward Snowden disclosures and how those transformation had affect the technology, business and politics in various countries. This paper describes in detail and explain how the disclosures done by Edward Snowden has happen, What is the most dangerous programs used by NSA to violate people privacy, The reaction of different countries such as USA and Canada toward this revelations and the change and transformation in policies, strategies and law regarding information security and privacy. Furthermore, how this revelations gave affect the relationship and make it more complicated between China and USA. In addition, many countries focus in developing law to protect their citizens’ privacy and security from any foreign surveillance. Moreover, how this revelations has been a lessons to NSA to strengthen its inside protection, lessons to companies to not trust to any internet company to store their business data and how to improve their security systems.
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.
How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".