Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Helen: Maliciously Secure Coopetitive Learning for Linear Models   Many organizations wish to collaboratively train machine learning models on
their combined datasets for a common benefit (e.g., better medical research, or
fraud detection). However, they often cannot share their plaintext datasets due
to privacy concerns and/or business competition. In this paper, we design and
build Helen, a system that allows multiple parties to train a linear model
without revealing their data, a setting we call coopetitive learning. Compared
to prior secure training systems, Helen protects against a much stronger
adversary who is malicious and can compromise m-1 out of m parties. Our
evaluation shows that Helen can achieve up to five orders of magnitude of
performance improvement when compared to training using an existing
state-of-the-art secure multi-party computation framework.

36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics