Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Secure and Differentially Private Bayesian Learning on Distributed Data   Data integration and sharing maximally enhance the potential for novel and
meaningful discoveries. However, it is a non-trivial task as integrating data
from multiple sources can put sensitive information of study participants at
risk. To address the privacy concern, we present a distributed Bayesian
learning approach via Preconditioned Stochastic Gradient Langevin Dynamics with
RMSprop, which combines differential privacy and homomorphic encryption in a
harmonious manner while protecting private information. We applied the proposed
secure and privacy-preserving distributed Bayesian learning approach to
logistic regression and survival analysis on distributed data, and demonstrated
its feasibility in terms of prediction accuracy and time complexity, compared
to the centralized approach.

29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph