Ensuring superior learning outcomes and data security for authorized learner

TL;DR

This paper introduces a quantum label encoding method within the PAC learning framework to guarantee superior learning outcomes for authorized learners while limiting eavesdroppers, validated through CNN image classification experiments.

Contribution

It provides a novel theorem ensuring authorized learning superiority using quantum encoding, based solely on data size and noise, unlike prior encryption-focused approaches.

Findings

Theorem guarantees authorized learner performance based on data size and noise.

Validation through CNN image classification confirms theoretical predictions.

Method enhances data security without relying solely on encryption.

Abstract

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.