Efficiency in Chat Application Encryption: A Comparative Review with Proposed Enhancements
Bibliographic record
Abstract
The explosive growth of real-time messaging applications has coincided with a major shift in focus towards digital privacy due to clear and present threats such as mass government surveillance, hacking and extortion, cyberbullying, and so on. This has created a need for implementations that provide sufficient confidentiality while working effectively within modern mobile devices. End-to-end encryption protocols like Off-The-Record (OTR) and Signal, that most chat applications adapt, ensure privacy but impose additional computational demands on devices with limited but varied resources. This paper compares WhatsApp, Telegram, and Signal, analyzing their resource consumption patterns during encrypted message transmission. By measuring CPU usage, GPU usage, and memory footprint, we evaluate how these applications manage workload to balance security and efficiency using modern hardware. We propose an encryption optimization approach with CPU offloading that min-imizes resource utilization while retaining acceptable standards for confidentiality, crucial for applications operating in mobile hardware.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".