Protecting neural networks from adversarial attacks
This project investigates how Searchable Symmetric Encryption (SSE) can be applied to neural networks as a form of protection from adversarial attacks and the viability of such an implementation. The implementation of SSE used is done in Python with the usage of single-keyword static SSE schemes,...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175267 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This project investigates how Searchable Symmetric Encryption (SSE) can be applied
to neural networks as a form of protection from adversarial attacks and the viability
of such an implementation. The implementation of SSE used is done in Python with
the usage of single-keyword static SSE schemes, applied to a neural network that is
generated using PyTorch. Metrics such as time taken for a search and space for the
database files are taken into account and then calculated for viability. Results show
that SSE is relevant for practical use in areas where the inputs can be controlled and
the resulting storage size of database will be a reasonable cost. The security benefits of
the SSE implementation will also have to outweigh the cost for it to see realistic use.
Other methods such as homomorphic encryption can be used on larger datasets and
more complex models and may even be able to allow models to be trained while fully
encrypted, protecting against more possibilities of adversarial attacks. |
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