Enabling Secure and Versatile Packet Inspection With Probable Cause Privacy for Outsourced Middlebox
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Middlebox is an intermediary network equipment which can be outsourced to remote cloud servers for low-cost and customizable network services, such as load balancer and intrusion detection. A fundamental function of the middlebox is packet inspection, where both the packet header and payload are extracted and analyzed based on inspection rules. However, as the packet may contain sensitive individual or organizational information, it may raise severe privacy concerns without proper countermeasures. In this article, we propose a secure and versatile packet inspection scheme for outsourced middlebox. The proposed scheme builds upon two non-collusion cloud servers, where the first server conducts the inspection task over the ciphertext domain and the second reveal the inspection results. By doing so, the proposed scheme achieves versatile inspection functionalities: range-query-based header inspection and token-based payload inspection, while preserving the privacy of packet header, payload, and inspection rules. Moreover, we identify and address two challenging issues in the state-of-the-art literatures. First, we tailor the design of mis-operation resistant searchable homomorphic encryption (MR-SHE) and somewhat homomorphic encryption in the two-server model, to resist <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline dictionary attack on payload headers</i> . Second, we propose a key management mechanism with compelled access for the middlebox, to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fine-grained probable cause privacy</i> . We also conduct extensive experiments and compare the results with existing schemes to demonstrate the feasibility of the proposed scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it