 [2305.11191] Towards Generalizable Data Protection With Transferable Unlearnable Examples




























  








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Computer Science > Cryptography and Security


arXiv:2305.11191 (cs)
    




  [Submitted on 18 May 2023]
Title:Towards Generalizable Data Protection With Transferable Unlearnable Examples
Authors:Bin Fang, Bo Li, Shuang Wu, Tianyi Zheng, Shouhong Ding, Ran Yi, Lizhuang Ma View a PDF of the paper titled Towards Generalizable Data Protection With Transferable Unlearnable Examples, by Bin Fang and Bo Li and Shuang Wu and Tianyi Zheng and Shouhong Ding and Ran Yi and Lizhuang Ma
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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.
    


 
Comments:
arXiv admin note: text overlap with arXiv:2305.10691


Subjects:

Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)

Cite as:
arXiv:2305.11191 [cs.CR]


 
(or 
arXiv:2305.11191v1 [cs.CR] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2305.11191



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                arXiv-issued DOI via DataCite
              







Submission history From: Bo Li [view email]       [v1]
        Thu, 18 May 2023 04:17:01 UTC (4,843 KB)



 

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