An Investigation of Privacy and Software Engineering in the Context of Quantum Computing
Bibliographic record
Abstract
This study delves into the crucial paradigm of privacy engineering for data protection in software design, specifically focusing on its application in Quantum Computing (QC). The relevance and importance of this focus must be considered. Implementing a full-scale QC requires time and heavy investment in IT infrastructure. The ultimate solution for deploying QC is a hybrid of classical computers interfacing with QC in hybrid clouds. While QC is a secure system by design, it has to interface with classical computers, which do not have such advanced security embedded in their design. The current study reviews the main QC and software developers to shed light on this hybrid integration. We also explore the recent NIST secure algorithms designed for such integration. Modern security algorithms such as RSA and ECC cannot continue in the era of quantum computing. Our study's findings are significant, as they highlight the potential of integrating the existing privacy engineering frameworks with advanced features of QC algorithms, particularly post-quantum cryptography (PQC) secure algorithms. In particular, the study highlights homomorphic and quantum key distribution algorithms for encryption and secure key distribution in a privacy-preserving approach.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".