Big Data Analytics: Security and privacy challenges
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Other designConsensus signal: none
- Genre
- Candidate signal: MethodsConsensus signal: none
- Teacher disagreement score
- 0.964
- Threshold uncertainty score
- 0.198
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.189 · how far apart the two teachers sit on this one work
- Validation status
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
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.
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.
The record
- Venue
- Topic
- Advanced Malware Detection Techniques
- Field
- Computer Science
- Canadian institutions
- University of Ottawa
- Funders
- not available
- Keywords
- Big dataComputer scienceData scienceAnalyticsContext (archaeology)Information privacyComputer securityData analysisInternet privacyData mining
- Has abstract in OpenAlex
- yes