Towards Generalizable Data Protection With Transferable Unlearnable Examples

• URL: http://arxiv.org/abs/2305.11191v1
• Date: Thu, 18 May 2023 04:17:01 GMT
• Title: Towards Generalizable Data Protection With Transferable Unlearnable Examples
• Authors: Bin Fang and Bo Li and Shuang Wu and Tianyi Zheng and Shouhong Ding and Ran Yi and Lizhuang Ma
• Abstract summary: We present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution.
• Score: 50.628011208660645
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial Intelligence (AI) is making a profound impact in almost every domain. One of the crucial factors contributing to this success has been the access to an abundance of high-quality data for constructing machine learning models. Lately, as the role of data in artificial intelligence has been significantly magnified, concerns have arisen regarding the secure utilization of data, particularly in the context of unauthorized data usage. To mitigate data exploitation, data unlearning have been introduced to render data unexploitable. However, current unlearnable examples lack the generalization required for wide applicability. In this paper, we present a novel, generalizable data protection method by generating transferable unlearnable examples. To the best of our knowledge, this is the first solution that examines data privacy from the perspective of data distribution. Through extensive experimentation, we substantiate the enhanced generalizable protection capabilities of our proposed method.

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