Differentially Private Bayesian Learning on Distributed Data

Mikko A. Heikkilä; Eemil Lagerspetz; Samuel Kaski; Kana Shimizu; Sasu Tarkoma; Antti Honkela

Differentially Private Bayesian Learning on Distributed Data

Mikko A. Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

42 Citations (Scopus)

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
Title of host publication	Advances in Neural Information Processing Systems 30
Subtitle of host publication	Proceedings of NIPS 2017
Publisher	Curran Associates Inc.
Pages	3227-3236
Number of pages	10
Publication status	Published - 2017
MoE publication type	A4 Conference publication
Event	IEEE Conference on Neural Information Processing Systems - Long Beach, United States Duration: 4 Dec 2017 → 9 Dec 2017 Conference number: 31

Publication series

Name	Advances in Neural Information Processing Systems
Publisher	Curran Associates
Volume	30
ISSN (Print)	1049-5258

Conference

Conference	IEEE Conference on Neural Information Processing Systems
Abbreviated title	NIPS
Country/Territory	United States
City	Long Beach
Period	04/12/2017 → 09/12/2017

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Cite this

Heikkilä, M. A., Lagerspetz, E., Kaski, S., Shimizu, K., Tarkoma, S., & Honkela, A. (2017). Differentially Private Bayesian Learning on Distributed Data. In Advances in Neural Information Processing Systems 30: Proceedings of NIPS 2017 (pp. 3227-3236). (Advances in Neural Information Processing Systems; Vol. 30). Curran Associates Inc.. https://arxiv.org/abs/1703.01106

@inproceedings{10560a11f22b48a78757740087854406,

title = "Differentially Private Bayesian Learning on Distributed Data",

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.",

author = "Heikkil{\"a}, \{Mikko A.\} and Eemil Lagerspetz and Samuel Kaski and Kana Shimizu and Sasu Tarkoma and Antti Honkela",

year = "2017",

language = "English",

series = "Advances in Neural Information Processing Systems",

publisher = "Curran Associates Inc.",

pages = "3227--3236",

booktitle = "Advances in Neural Information Processing Systems 30",

address = "United States",

note = "IEEE Conference on Neural Information Processing Systems, NIPS ; Conference date: 04-12-2017 Through 09-12-2017",

}

Heikkilä, MA, Lagerspetz, E, Kaski, S, Shimizu, K, Tarkoma, S & Honkela, A 2017, Differentially Private Bayesian Learning on Distributed Data. in Advances in Neural Information Processing Systems 30: Proceedings of NIPS 2017. Advances in Neural Information Processing Systems, vol. 30, Curran Associates Inc., pp. 3227-3236, IEEE Conference on Neural Information Processing Systems, Long Beach, California, United States, 04/12/2017. <https://arxiv.org/abs/1703.01106>

Differentially Private Bayesian Learning on Distributed Data. / Heikkilä, Mikko A.; Lagerspetz, Eemil; Kaski, Samuel et al.
Advances in Neural Information Processing Systems 30: Proceedings of NIPS 2017. Curran Associates Inc., 2017. p. 3227-3236 (Advances in Neural Information Processing Systems; Vol. 30).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Differentially Private Bayesian Learning on Distributed Data

AU - Heikkilä, Mikko A.

AU - Lagerspetz, Eemil

AU - Kaski, Samuel

AU - Shimizu, Kana

AU - Tarkoma, Sasu

AU - Honkela, Antti

N1 - Conference code: 31

PY - 2017

Y1 - 2017

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.

UR - https://www.scopus.com/pages/publications/85047010172

M3 - Conference article in proceedings

T3 - Advances in Neural Information Processing Systems

SP - 3227

EP - 3236

BT - Advances in Neural Information Processing Systems 30

PB - Curran Associates Inc.

T2 - IEEE Conference on Neural Information Processing Systems

Y2 - 4 December 2017 through 9 December 2017

ER -

Differentially Private Bayesian Learning on Distributed Data

Abstract

Publication series

Conference

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PADS: Privacy-aware Data Science (PADS) - Yksityisyystietoinen datatiede

Interactive machine learning from multiple biodata sources

Interactive machine learning from multiple biodata sources

Cite this

Differentially Private Bayesian Learning on Distributed Data

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Projects

PADS: Privacy-aware Data Science (PADS) - Yksityisyystietoinen datatiede

Interactive machine learning from multiple biodata sources

Interactive machine learning from multiple biodata sources

Cite this