An Intent-Based Networking Framework for Secure and Privacy-Compliant Machine Unlearning Using Meta-Learning and Redactable Blockchain
Bibliographic record
Abstract
Intent-Based Networking (IBN) is emerging as a powerful paradigm for managing complex, adaptive systems by translating high-level user policies into automated infrastructure behavior. To meet these intents effectively, especially in dynamic and data-driven environments, IBN increasingly depends on Artificial Intelligence (AI) for intelligent decision-making and task automation. While AI enhances the responsiveness of IBN, it introduces new challenges in data privacy and regulatory compliance—particularly in environments where sensitive personal data is continuously collected and learned. A central issue arises from the Right to Be Forgotten (RTBF) under the General Data Protection Regulation (GDPR), which requires that user data—and its learned influence—be fully removed upon request. However, conventional unlearning methods that rely on full model retraining are resource-intensive and impractical for real-time systems. To address this, this paper proposes an intent-driven machine unlearning framework that integrates meta learning, redactable blockchain, and Secure Multi-Party Computation (MPC), all coordinated through IBN. In this framework, unlearning is formulated as a targeted removal of a data point’s influence from the model without retraining, using implicit gradients. The redactable blockchain ensures compliant and auditable logging, while MPC supports secure, decentralized redaction. IBN orchestrates the process by aligning unlearning actions with privacy intents. Experimental results demonstrate that the proposed framework achieves up to 2.2% higher accuracy than baseline meta-learning approaches and 2.78 times greater unlearning efficiency compared to retraining, while maintaining strong resistance to membership inference attacks. This demonstrates its suitability for scalable, regulation-compliant AI unlearning.
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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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".