A Privacy-Preserving Surveillance Video Sharing Scheme: Storage, Authentication, and Joint Retrieval
Bibliographic record
Abstract
Abstract Today, video surveillance systems are widely used to oversee and enhance home and premises security. For convenience and security, users often upload surveillance footage to the cloud, which can be accessed by trusted users during emergencies. However, given the sensitive nature of this data, users might be wary of trusting cloud services with user authentication and video access. Thus, managing video access control and user authentication with limited trust in cloud services presents a significant challenge. In this paper, we propose a privacy-preserving and secure scheme for storing and sharing video footage on the cloud. Homeowners can incorporate their trusted individuals into the system, thereby granting them access to view cloud-stored videos. Even in an emergency, these trusted users can collaborate to retrieve videos securely from the cloud. Our scheme allows homeowners to authenticate users and track video access without relying on cloud services. Our analysis by conducting formal proof using the Real-Or-Random (ROR) model indicates that the proposed scheme is secure. Additionally, we confirm its security and authenticity through Proverif, a formal verification tool. Informal security assessments further demonstrate its resilience against various attacks. Additionally, our communication and computational cost assessments show that our scheme is an effective and acceptable solution.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".