TY - GEN
T1 - SecretGen
T2 - 17th European Conference on Computer Vision, ECCV 2022
AU - Yuan, Zhuowen
AU - Wu, Fan
AU - Long, Yunhui
AU - Xiao, Chaowei
AU - Li, Bo
N1 - Funding Information:
Acknowledgements. This work is partially supported by NSF grant No.1910100, NSF CNS No. 2046726, C3 AI, and the Alfred P. Sloan Foundation.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Transfer learning through the use of pre-trained models has become a growing trend for the machine learning community. Consequently, numerous pre-trained models are released online to facilitate further research. However, it raises extensive concerns on whether these pre-trained models would leak privacy-sensitive information of their training data. Thus, in this work, we aim to answer the following questions: “Can we effectively recover private information from these pre-trained models? What are the sufficient conditions to retrieve such sensitive information?” We first explore different statistical information which can discriminate the private training distribution from other distributions. Based on our observations, we propose a novel private data reconstruction framework, SecretGen, to effectively recover private information. Compared with previous methods which can recover private data with the ground truth label of the targeted recovery instance, SecretGen does not require such prior knowledge, making it more practical. We conduct extensive experiments on different datasets under diverse scenarios to compare SecretGen with other baselines and provide a systematic benchmark to better understand the impact of different auxiliary information and optimization operations. We show that without prior knowledge about true class prediction, SecretGen is able to recover private data with similar performance compared with the ones that leverage such prior knowledge. If the prior knowledge is given, SecretGen will significantly outperform baseline methods. We also propose several quantitative metrics to further quantify the privacy vulnerability of pre-trained models, which will help the model selection for privacy-sensitive applications. Our code is available at: https://github.com/AI-secure/SecretGen.
AB - Transfer learning through the use of pre-trained models has become a growing trend for the machine learning community. Consequently, numerous pre-trained models are released online to facilitate further research. However, it raises extensive concerns on whether these pre-trained models would leak privacy-sensitive information of their training data. Thus, in this work, we aim to answer the following questions: “Can we effectively recover private information from these pre-trained models? What are the sufficient conditions to retrieve such sensitive information?” We first explore different statistical information which can discriminate the private training distribution from other distributions. Based on our observations, we propose a novel private data reconstruction framework, SecretGen, to effectively recover private information. Compared with previous methods which can recover private data with the ground truth label of the targeted recovery instance, SecretGen does not require such prior knowledge, making it more practical. We conduct extensive experiments on different datasets under diverse scenarios to compare SecretGen with other baselines and provide a systematic benchmark to better understand the impact of different auxiliary information and optimization operations. We show that without prior knowledge about true class prediction, SecretGen is able to recover private data with similar performance compared with the ones that leverage such prior knowledge. If the prior knowledge is given, SecretGen will significantly outperform baseline methods. We also propose several quantitative metrics to further quantify the privacy vulnerability of pre-trained models, which will help the model selection for privacy-sensitive applications. Our code is available at: https://github.com/AI-secure/SecretGen.
KW - Pre-trained models
KW - Privacy
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85144519485&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144519485&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-20065-6_9
DO - 10.1007/978-3-031-20065-6_9
M3 - Conference contribution
AN - SCOPUS:85144519485
SN - 9783031200649
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 139
EP - 155
BT - Computer Vision – ECCV 2022 - 17th European Conference, Proceedings
A2 - Avidan, Shai
A2 - Brostow, Gabriel
A2 - Cissé, Moustapha
A2 - Farinella, Giovanni Maria
A2 - Hassner, Tal
PB - Springer
Y2 - 23 October 2022 through 27 October 2022
ER -