Preserving differential privacy has been well studied
under the centralized setting. However, it’s very challenging to
preserve differential privacy under multiparty setting, especially
for the vertically partitioned case. In this work, we propose a
new framework for differential privacy preserving multiparty
learning in the vertically partitioned setting. Our core idea is
based on the functional mechanism that achieves differential
privacy of the released model by adding noise to the objective
function. We show the server can simply dissect the objective
function into single-party and cross-party sub-functions, and
allocate computation and perturbation of their polynomial coefficients
t o l ocal p arties. Our method n eeds o nly o ne r ound of
noise addition and secure aggregation. The released model in our
framework achieves the same utility as applying the functional
mechanism in the centralized setting. Evaluation on real-world
and synthetic datasets for linear and logistic regressions shows
the effectiveness of our proposed method.
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Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Distributed learning allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. We present a distributed learning approach that combines differential privacy with secure multi-party computation. We explore two popular methods of differential privacy, output perturbation and gradient perturbation, and advance the state-of-the-art for both methods in the distributed learning setting. In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model. In our gradient perturbation method, the data owners collaboratively train a global model via an iterative learning algorithm. At each iteration, the parties aggregate their local gradients within a secure computation, adding sufficient noise to ensure privacy before the gradient updates are revealed. For both methods, we show that the noise can be reduced in the multi-party setting by adding the noise inside the secure computation after aggregation, asymptotically improving upon the best previous results. Experiments on real world data sets demonstrate that our methods provide substantial utility gains for typical privacy requirements.
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- Award ID(s):
- 1717950
- NSF-PAR ID:
- 10099283
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
