  Secure similar document detection (SSDD) identifies similar documents of two
parties while each party does not disclose its own sensitive documents to
another party. In this paper, we propose an efficient 2-step protocol that
exploits a feature selection as the lower-dimensional transformation and
presents discriminative feature selections to maximize the performance of the
protocol. For this, we first analyze that the existing 1-step protocol causes
serious computation and communication overhead for high dimensional document
vectors. To alleviate the overhead, we next present the feature selection-based
2-step protocol and formally prove its correctness. The proposed 2-step
protocol works as follows: (1) in the filtering step, it uses low dimensional
vectors obtained by the feature selection to filter out non-similar documents;
(2) in the post-processing step, it identifies similar documents only from the
non-filtered documents by using the 1-step protocol. As the feature selection,
we first consider the simplest one, random projection (RP), and propose its
2-step solution SSDD-RP. We then present two discriminative feature selections
and their solutions: SSDD-LF (local frequency) which selects a few dimensions
locally frequent in the current querying vector and SSDD-GF (global frequency)
which selects ones globally frequent in the set of all document vectors. We
finally propose a hybrid one, SSDD-HF (hybrid frequency), that takes advantage
of both SSDD-LF and SSDD-GF. We empirically show that the proposed 2-step
protocol outperforms the 1-step protocol by three or four orders of magnitude.
