Privacy-Preserving Efficient Verifiable Deep Packet Inspection for Cloud-Assisted Middlebox
Bibliographic record
Abstract
With the increasing traffic volume, enterprises choose to outsource their middlebox services, such as deep packet inspection, to the cloud to acquire rich computational and communication resources. However, since the traffic is redirected to the public cloud, information leakages, such as packet payload and inspection rules, arouse privacy concerns of both middlebox owner and packet senders. To address the concerns, we propose an efficient verifiable deep packet inspection (EV-DPI) scheme with strong privacy guarantees. Specifically, a two-layer architecture is designed and deployed over two non-collusion cloud servers. The first layer fast filters out most of legitimate packets and the second layer supports exact rule matching. During the inspection, the privacy of packet payload and the confidentiality of inspection rules are well preserved. To improve the efficiency, only fast symmetric crypto-systems, such as hash functions, are used. Moreover, the proposed scheme allows the network administrator to verify the execution results, which offers a strong control of outsourced services. To validate the performance of the proposed EV-DPI scheme, we conduct extensive experiments on the Amazon Cloud. Large-scale dataset (millions of packets) is tested to obtain the key performance metrics. The experimental results demonstrate that EV-DPI not only preserves the packet privacy, but also achieves high packet inspection efficiency.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".