VerifyTL: Secure and Verifiable Collaborative Transfer Learning

• Main Authors: MA, Zhuoran, MA, Jianfeng, MIAO, Yinbin, LIU, Ximeng, ZHENG, Wei, CHOO, Kim-Kwang Raymond, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Collaboration; Computational modeling; Convolutional neural network; Dishonest majority; Malicious security; Protocols; Security; SPDZ; Training; Training data; Transfer learning; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7804; https://ink.library.smu.edu.sg/context/sis_research/article/8807/viewcontent/VerifyTL_av.pdf
• Institution: Singapore Management University

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.