Abstract
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness, or add prohibitive amounts of noise. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.
| Original language | English |
|---|---|
| Pages (from-to) | 3227-3236 |
| Number of pages | 10 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 2017-December |
| Publication status | Published - 2017 Jan 1 |
| Event | 31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States Duration: 2017 Dec 4 → 2017 Dec 9 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
Differentially private Bayesian learning on distributed data. / Heikkilä, Mikko; Lagerspetz, Eemil; Kaski, Samuel; Shimizu, Kana; Tarkoma, Sasu; Honkela, Antti.
In: Advances in Neural Information Processing Systems, Vol. 2017-December, 01.01.2017, p. 3227-3236.Research output: Contribution to journal › Conference article
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TY - JOUR
T1 - Differentially private Bayesian learning on distributed data
AU - Heikkilä, Mikko
AU - Lagerspetz, Eemil
AU - Kaski, Samuel
AU - Shimizu, Kana
AU - Tarkoma, Sasu
AU - Honkela, Antti
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness, or add prohibitive amounts of noise. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.
AB - Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness, or add prohibitive amounts of noise. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.
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UR - http://www.scopus.com/inward/citedby.url?scp=85047010172&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85047010172
VL - 2017-December
SP - 3227
EP - 3236
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
SN - 1049-5258
ER -