Deep Neural Networks Classification over Encrypted Data
Bibliographic record
Abstract
Deep Neural Networks (DNNs) have overtaken classic machine learning algorithms due to their superior performance in big data analysis in a broad range of applications. On the other hand, in recent years Machine Learning as a Service (MLaaS) has become more widespread in which a client uses cloud services for analyzing its data. However, the client's data may be sensitive which raises privacy concerns. In this paper, we address the issue of privacy preserving classification in a Machine Learning as a Service (MLaaS) settings and focus on convolutional neural networks (CNN). To achieve this goal, we develop new techniques to run CNNs over encrypted data. First, we design methods to approximate commonly used activation functions in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for a practical and efficient solution. Then, we train CNNs with approximation polynomials instead of original activation functions and implement CNNs classification over encrypted data. We evaluate the performance of our modified models at each step. The results of our experiments using several CNNs with a varying number of layers and structures are promising. When applied to the MNIST optical character recognition tasks, our approach achieved 99.25% accuracy which significantly outperforms state-of-the-art solutions and is close to the accuracy of the best non-private version. Furthermore, it can make up to 164000 predictions per hour. These results show that our approach provides accurate, efficient, and scalable privacy-preserving predictions in CNNs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".