Privacy Framework for Open Environments
Bibliographic record
Abstract
With the explosion of internet services, new types of applications are emerged that are steered by self-interested entities. As there is no central control on these entities and they autonomously interact and deliver services to others, it creates an open environment that is the context of various types of nowadays applications. Formally, it can be described as Cooperative Distributed Systems in which interacting entities are autonomous and self-interested and they need capabilities of others to achieve their goals. Sensitive information is exchanged between entities while they are interacting. Exploiting sensitive information may violate entities' privacy. This paper proposes a privacy model that captures privacy solutions at the interaction level. This work provides analysis of privacy issues in interactions of entities and provides solutions to reduce the risk of privacy violation in CDS. Accordingly, it results in a privacy framework that is associable in information systems and architectures. Integration of the proposed framework and scheduling solution as an example of information systems is presented in this paper as well. Also, the implementation details of applying privacy framework on architectural platforms are provided.
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 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".