Big Data Analytics: Security and privacy challenges
Bibliographic record
Abstract
The digitalization of our day-to-day activities has resulted in a huge volume of data. This data, called Big Data, is used by many organizations to extract valuable information either to take marketing decisions, track specific behaviors or detect threat attacks. The processing of such data is made possible by using multiple techniques, called Big Data Analytics, which allow getting enormous benefits by dealing with any massive volume of unstructured, structured and semi-structured content that is fast changing and impossible to process using conventional database techniques. However, while Big Data represents an immense opportunity for many industries and decisions makers, it also represents a big risk for many users. This risk arises from the fact that these analytics tools consist of storing, managing and efficiently analyzing varied data gathered from all possible and available sources. The consequence is that people become widely vulnerable to exposure because of combining and exploring specific behavioral data. That is, it is possible to collect more data than it should have which leads to many security and privacy violations. Therefore, research community has to consider these issues by proposing strong protection techniques that enable getting benefits from big data without risking privacy. In this paper, we highlight the benefits of Big Data Analytics and then we review challenges of security and privacy in big data environments. Furthermore, we present some available protection techniques and propose some possible tracks that enable security and privacy in a malicious big data context.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".