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, our
proposed method aims to trap the attackers in a misleading feature distillation
of the data. To achieve this goal, our AdvEncryption method consists of two
essential components: 1) an adversarial attack-inspired encryption mechanism to
encrypt the data with stealthy adversarial perturbation, and 2) a decryption
mechanism that minimizes the impact of the perturbations on the effectiveness
of autonomous decision making. In the performance evaluation section, we
evaluate the performance of our proposed AdvEncryption method through case
studies considering different scenarios.

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