  Federated knowledge discovery and data mining are challenged to assess the
trustworthiness of data originating from autonomous sources while protecting
confidentiality and privacy. Truth-finding algorithms help corroborate data
from disagreeing sources. For each query it receives, a truth-finding algorithm
predicts a truth value of the answer, possibly updating the trustworthiness
factor of each source. Few works, however, address the issues of
confidentiality and privacy. We devise and present a secure
secret-sharing-based multi-party computation protocol for pseudo-equality tests
that are used in truth-finding algorithms to compute additions depending on a
condition. The protocol guarantees confidentiality of the data and privacy of
the sources. We also present variants of truth-finding algorithms that would
make the computation faster when executed using secure multi-party computation.
We empirically evaluate the performance of the proposed protocol on two
state-of-the-art truth-finding algorithms, Cosine, and 3-Estimates, and compare
them with that of the baseline plain algorithms. The results confirm that the
secret-sharing-based secure multi-party algorithms are as accurate as the
corresponding baselines but for proposed numerical approximations that
significantly reduce the efficiency loss incurred.
