TransMIA: Membership Inference Attacks Using Transfer Shadow Training
| Autor: | Yusuke Kawamoto, Seira Hidano, Takao Murakami |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Cryptography and Security Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Computer Science - Computer Vision and Pattern Recognition Inference Construct (python library) Adversary Machine learning computer.software_genre Machine Learning (cs.LG) Transfer (computing) Shadow Entropy (information theory) Artificial intelligence Transfer of learning business Cryptography and Security (cs.CR) computer |
| Zdroj: | IJCNN |
| Popis: | Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different training. However, no prior work has pointed out that transfer learning can strengthen privacy attacks on machine learning models. In this paper, we propose TransMIA (Transfer learning-based Membership Inference Attacks), which use transfer learning to perform membership inference attacks on the source model when the adversary is able to access the parameters of the transferred model. In particular, we propose a transfer shadow training technique, where an adversary employs the parameters of the transferred model to construct shadow models, to significantly improve the performance of membership inference when a limited amount of shadow training data is available to the adversary. We evaluate our attacks using two real datasets, and show that our attacks outperform the state-of-the-art that does not use our transfer shadow training technique. We also compare four combinations of the learning-based/entropy-based approach and the fine-tuning/freezing approach, all of which employ our transfer shadow training technique. Then we examine the performance of these four approaches based on the distributions of confidence values, and discuss possible countermeasures against our attacks. Comment: IJCNN 2021 conference paper |
| Databáze: | OpenAIRE |
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