Theory and application of encrypted sequential data processing: search and computation
Bibliographic record
Abstract
Cloud Computing has seen a dramatic rise in adoption in the past decade amid se- curity and privacy concerns. One area of consensus is that encryption is necessary, as anonymization techniques have been shown to be unreliable. However, the processing of encrypted data has proven to be difficult. Briefly, the goal is to maintain security over remotely stored and accessed data while achieving reasonable storage cost and perfor- mance. Search is the most basic and central functionality of a privacy-protected cloud storage system actively being investigated. Recent works have looked at enabling more specialized search functions. In this thesis, we explore the problem of searching and pro- cessing of sequential data. We propose three solutions targeting textual data, with em- phasis respectively on security, storage cost and performance. Our first solution achieves a high level of security with reduced communication, storage and computational cost by exploiting properties of natural languages. Our second solution achieves a minimal storage cost by taking advantage of the space efficiency of Bloom filters. Both propos- als were also first to enable non-keyword search in phrases. Using a subsequence-based solution, our final phrase search scheme is currently the fastest phrase search protocol in literature. We also show how sequential data search schemes can be extended to in- clude auditing with minimal additional cost. The solution is capable of achieving proof of retrievability with unbounded number of audits. A sample application which enables searching and computing over target values of encrypted XML files is also demonstrated. In terms of media, we describe an encrypted cloud media storage solution that simultane- ously protects user privacy and enables copyright verification, and is the first to achieve security against dishonest participants. We also describe a framework where practical scalable privacy-protected copyright detection can be performed. Finally, an application of sequence querying over generic data in the form of an Anti-Virus over encrypted cloud storage is demonstrated. A private scanning solution and a public Anti-Virus as a ser- vice solution are described, noting that the technique can be conceptualized as a generic pattern matching solution on encrypted data. We also include some directions on future work and unexplored applications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".