Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PPCA: Privacy-preserving Principal Component Analysis Using Secure
  Multiparty Computation(MPC)   Privacy-preserving data mining has become an important topic. People have
built several multi-party-computation (MPC)-based frameworks to provide
theoretically guaranteed privacy, the poor performance of real-world algorithms
have always been a challenge. Using Principal Component Analysis (PCA) as an
example, we show that by considering the unique performance characters of the
MPC platform, we can design highly effective algorithm-level optimizations,
such as replacing expensive operators and batching up. We achieve about
200$\times$ performance boost over existing privacy-preserving PCA algorithms
with the same level of privacy guarantee. Also, using real-world datasets, we
show that by combining multi-party data, we can achieve better training
results.

22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat