Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2023

Abstract

Getting access to labeled datasets in certain sensitive application domains can be challenging. Hence, one may resort to transfer learning to transfer knowledge learned from a source domain with sufficient labeled data to a target domain with limited labeled data. However, most existing transfer learning techniques only focus on one-way transfer which may not benefit the source domain. In addition, there is the risk of a malicious adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper, we construct a secure and Verif iable collaborative T ransfer L earning scheme, VerifyTL, to support two-way transfer learning over potentially untrusted datasets by improving knowledge transfer from a target domain to a source domain. Furthermore, we equip VerifyTL with a secure and verifiable transfer unit employing SPDZ computation to provide privacy guarantee and verification in the multi-domain setting. Thus, VerifyTL is secure against malicious adversary that can compromise up to n−1 out of n data domains. We analyze the security of VerifyTL and evaluate its performance over four real-world datasets. Experimental results show that VerifyTL achieves significant performance gains over existing secure learning schemes.

Keywords

Collaboration, Computational modeling, Convolutional neural network, Dishonest majority, Malicious security, Protocols, Security, SPDZ, Training, Training data, Transfer learning, Transfer learning

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Dependable and Secure Computing

First Page

1

Last Page

14

ISSN

1545-5971

Identifier

10.1109/TDSC.2023.3241181

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TDSC.2023.3241181

Download

Share

COinS