Privacy of Deep Learning Systems: A Penetration Testing Framework
Bibliographic record
Abstract
The advent of deep learning has revolutionized various data-driven fields such as image recognition, natural language processing, and autonomous vehicles. Despite its transformative potential, deep learning raises significant privacy concerns, particularly regarding the handling of sensitive data during training and inference. This study systematically reviews existing literature on privacy-preserving techniques in deep learning systems, addressing three primary research questions: the main privacy concerns, the effectiveness of current penetration testing techniques, and the mitigation strategies to enhance privacy. Privacy concerns primarily revolve around the risk of exposing sensitive training data and internal model parameters through attacks like model inversion. Differential privacy and homomorphic encryption are widely employed to mitigate these risks, although challenges remain in balancing privacy with model utility. Penetration testing techniques, such as adversarial attack simulations and differential privacy analysis, play a crucial role in identifying vulnerabilities but often lack comprehensive coverage across all stages of a deep learning system's lifecycle. Mitigation strategies following penetration testing include robust data anonymization, encryption, differential privacy mechanisms, and federated learning to protect data during transfer and storage. Continuous monitoring, regular audits, and incident response procedures are also essential to maintain privacy standards and ensure system resilience. This research highlights the need for integrating comprehensive privacy measures throughout the lifecycle of deep learning systems. Future research directions include the development of more effective penetration testing methodologies and enhanced privacy-preserving algorithms to safeguard sensitive data and maintain user trust.
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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".