Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving
  Cooperative Learning   Cooperative learning, that enables two or more data owners to jointly train a
model, has been widely adopted to solve the problem of insufficient training
data in machine learning. Nowadays, there is an urgent need for institutions
and organizations to train a model cooperatively while keeping each other's
data privately. To address the issue of privacy-preserving in collaborative
learning, secure outsourced computation and federated learning are two typical
methods. Nevertheless, there are many drawbacks for these two methods when they
are leveraged in cooperative learning. For secure outsourced computation,
semi-honest servers need to be introduced. Once the outsourced servers collude
or perform other active attacks, the privacy of data will be disclosed. For
federated learning, it is difficult to apply to the scenarios where vertically
partitioned data are distributed over multiple parties. In this work, we
propose a multi-party mixed protocol framework, ABG$^n$, which effectively
implements arbitrary conversion between Arithmetic sharing (A), Boolean sharing
(B) and Garbled-Circuits sharing (G) for $n$-party scenarios. Based on ABG$^n$,
we design a privacy-preserving multi-party cooperative learning system, which
allows different data owners to cooperate in machine learning in terms of data
security and privacy-preserving. Additionally, we design specific
privacy-preserving computation protocols for some typical machine learning
methods such as logistic regression and neural networks. Compared with previous
work, the proposed method has a wider scope of application and does not need to
rely on additional servers. Finally, we evaluate the performance of ABG$^n$ on
the local setting and on the public cloud setting. The experiments indicate
that ABG$^n$ has excellent performance, especially in the network environment
with low latency.

4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: math
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The categories of this paper are: dg-ga