IFCaaS: Information Flow Control as a Service for Cloud Security
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
With the maturity of service-oriented architecture (SOA) and Web technologies, web services have become critical components of Software as a Service (SaaS) applications in cloud ecosystem environments. Most SaaS applications leverage multi-tenant data stores as a back end to keep and process data with high agility. Although these technologies promise impressive benefits, they put SaaS applications at risk against novel as well as prevalent attack vectors. This security risk is further magnified by the loss of control and lack of security enforcement over sensitive data manipulated by SaaS applications. An effective solution is needed to fulfill several requirements originating in the dynamic and complex nature of such applications. Inspired by the rise of Security as a Service (SecaaS) model, this paper introduces "Information Flow Control as a Service (IFCaaS)". IFCaaS lays the foundation of cloud-delivered IFC-based security analysis and monitoring services. As an example of the adoption of the IFCaaS, this paper presents a novel framework that addresses the detection of information flow vulnerabilities in SaaS applications. Our initial experiments show that the framework is a viable solution to protect against data integrity and confidentiality violations leading to information leakage.
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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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