ML Security PapersEnsuring superior learning outcomes and data security for authorized learner
Jeongho Bang 1, Wooyeong Song 2, Kyujin Shin 3,4, Yong-Su Kim 4,5
Published on arXiv
2501.00754
Model Inversion Attack
OWASP ML Top 10 — ML03
Key Finding
A theorem proves that, given quantum label encoding, an authorized learner is guaranteed a superior learning outcome over eavesdroppers, with the condition expressible entirely from dataset size and noise degree, confirmed on CNN image classifiers.
Quantum label encoding
Novel technique introduced
The learner's ability to generate a hypothesis that closely approximates the target function is crucial in machine learning. Achieving this requires sufficient data; however, unauthorized access by an eavesdropping learner can lead to security risks. Thus, it is important to ensure the performance of the "authorized" learner by limiting the quality of the training data accessible to eavesdroppers. Unlike previous studies focusing on encryption or access controls, we provide a theorem to ensure superior learning outcomes exclusively for the authorized learner with quantum label encoding. In this context, we use the probably-approximately-correct (PAC) learning framework and introduce the concept of learning probability to quantitatively assess learner performance. Our theorem allows the condition that, given a training dataset, an authorized learner is guaranteed to achieve a certain quality of learning outcome, while eavesdroppers are not. Notably, this condition can be constructed based only on the authorized-learning-only measurable quantities of the training data, i.e., its size and noise degree. We validate our theoretical proofs and predictions through convolutional neural networks (CNNs) image classification learning.
Key Contributions
- Introduces the concept of 'learning probability' within the PAC learning framework to quantitatively compare authorized vs. eavesdropping learner performance
- Provides a theorem guaranteeing exclusive superior learning outcomes for the authorized learner using quantum label encoding, derived solely from measurable dataset properties (size and noise degree)
- Validates theoretical predictions empirically through CNN-based image classification experiments
🛡️ Threat Analysis
The paper encodes training labels using quantum encoding specifically to prevent an eavesdropping adversary from obtaining useful training information from intercepted data — the adversary is an unauthorized learner trying to exploit the same training dataset, and the defense degrades eavesdropper learning quality while preserving authorized learner performance.