  Getting access to labelled datasets in certain sensitive application domains
can be challenging. Hence, one often resorts to transfer learning to transfer
knowledge learned from a source domain with sufficient labelled data to a
target domain with limited labelled data. However, most existing transfer
learning techniques only focus on one-way transfer which brings no benefit to
the source domain. In addition, there is the risk of a covert adversary
corrupting a number of domains, which can consequently result in inaccurate
prediction or privacy leakage. In this paper we construct a secure and
Verifiable collaborative Transfer Learning scheme, VerifyTL, to support two-way
transfer learning over potentially untrusted datasets by improving knowledge
transfer from a target domain to a source domain. Further, we equip VerifyTL
with a cross transfer unit and a weave transfer unit employing SPDZ computation
to provide privacy guarantee and verification in the two-domain setting and the
multi-domain setting, respectively. Thus, VerifyTL is secure against covert
adversary that can compromise up to n-1 out of n data domains. We analyze the
security of VerifyTL and evaluate its performance over two real-world datasets.
Experimental results show that VerifyTL achieves significant performance gains
over existing secure learning schemes.
