A Novel Data Encryption Method Inspired by Adversarial Attacks
Praveen Fernando, Jin Wei-Kocsis

TL;DR
This paper introduces AdvEncryption, a novel data encryption technique inspired by adversarial attacks, designed to mislead attackers while preserving data utility for AI systems.
Contribution
It presents a new encryption method that uses adversarial perturbations to trap attackers, differing from traditional encryption by focusing on misleading attackers rather than preventing data access.
Findings
Effective in misleading attackers with minimal impact on AI decision accuracy
Demonstrated robustness across various scenarios
Balances data security with usability
Abstract
Due to the advances of sensing and storage technologies, a tremendous amount of data becomes available and, it supports the phenomenal growth of artificial intelligence (AI) techniques especially, deep learning (DL), in various application domains. While the data sources become valuable assets for enabling the success of autonomous decision-making, they also lead to critical vulnerabilities in privacy and security. For example, data leakage can be exploited via querying and eavesdropping in the exploratory phase for black-box attacks against DL-based autonomous decision-making systems. To address this issue, in this work, we propose a novel data encryption method, called AdvEncryption, by exploiting the principle of adversarial attacks. Different from existing encryption technologies, the AdvEncryption method is not developed to prevent attackers from exploiting the dataset. Instead,…
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Taxonomy
TopicsCryptography and Data Security · Adversarial Robustness in Machine Learning · Chaos-based Image/Signal Encryption
