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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.09813 |
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| _version_ | 1866911754962862080 |
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| author | Zhou, Juexiao Li, Haoyang Liao, Xingyu Zhang, Bin He, Wenjia Li, Zhongxiao Zhou, Longxi Gao, Xin |
| author_facet | Zhou, Juexiao Li, Haoyang Liao, Xingyu Zhang, Bin He, Wenjia Li, Zhongxiao Zhou, Longxi Gao, Xin |
| contents | Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients' data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients' right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available at \url{https://github.com/JoshuaChou2018/AFS}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_09813 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare Zhou, Juexiao Li, Haoyang Liao, Xingyu Zhang, Bin He, Wenjia Li, Zhongxiao Zhou, Longxi Gao, Xin Machine Learning Cryptography and Security Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients' data are being collected and used for model training, especially in intelligent healthcare, thus making intelligent healthcare a sector where technology must meet the law, regulations, and privacy principles to ensure that the innovation is for the common good. In order to secure patients' right to be forgotten, we proposed a novel solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing a new approach called knowledge purification. To implement our solution, we developed AFS, a unified open-source software, which is able to evaluate and revoke patients' private data from pre-trained deep learning models. We demonstrated the generality of AFS by applying it to four tasks on different datasets with various data sizes and architectures of deep learning networks. The software is publicly available at \url{https://github.com/JoshuaChou2018/AFS}. |
| title | Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2302.09813 |