---
OA_place: publisher
OA_type: diamond
_id: '20298'
abstract:
- lang: eng
text: "In this paper, we study the problem of estimating the unknown mean θ of a
unit variance Gaussian distribution in a locally differentially private (LDP)
way. In the high-privacy regime (ϵ≤1\r\n), we identify an optimal privacy mechanism
that minimizes the variance of the estimator asymptotically. Our main technical
contribution is the maximization of the Fisher-Information of the sanitized data
with respect to the local privacy mechanism Q. We find that the exact solution
Qθ,ϵ of this maximization is the sign mechanism that applies randomized response
to the sign of Xi−θ, where X1,…,Xn are the confidential iid original samples.
However, since this optimal local mechanism depends on the unknown mean θ, we
employ a two-stage LDP parameter estimation procedure which requires splitting
agents into two groups. The first n1 observations are used to consistently but
not necessarily efficiently estimate the parameter θ by θn1~\r\n. Then this estimate
is updated by applying the sign mechanism with θ~n1 instead of θ\r\n to the remaining
n−n1 observations, to obtain an LDP and efficient estimator of the unknown mean."
acknowledgement: "We would like to express our gratitude to Christoph Lampert for
his valuable insights and fruitful discussions that significantly contributed to
the development of this paper.\r\nWe also thank Salil Vadhan for his constructive
feedback on an earlier version of this draft.\r\nThe second author gratefully acknowledges
support by the Austrian Science Fund (FWF): I 5484-N, as part of the Research Unit
5381 of the German Research Foundation."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikita
full_name: Kalinin, Nikita
id: 4b14526e-14d2-11ed-ba64-c14c9553d137
last_name: Kalinin
- first_name: Lukas
full_name: Steinberger, Lukas
last_name: Steinberger
citation:
ama: 'Kalinin N, Steinberger L. Efficient estimation of a Gaussian mean with local
differential privacy. In: Proceedings of the 28th International Conference
on Artificial Intelligence and Statistics. Vol 258. ML Research Press; 2025:118-126.'
apa: 'Kalinin, N., & Steinberger, L. (2025). Efficient estimation of a Gaussian
mean with local differential privacy. In Proceedings of the 28th International
Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 118–126).
Mai Khao, Thailand: ML Research Press.'
chicago: Kalinin, Nikita, and Lukas Steinberger. “Efficient Estimation of a Gaussian
Mean with Local Differential Privacy.” In Proceedings of the 28th International
Conference on Artificial Intelligence and Statistics, 258:118–26. ML Research
Press, 2025.
ieee: N. Kalinin and L. Steinberger, “Efficient estimation of a Gaussian mean with
local differential privacy,” in Proceedings of the 28th International Conference
on Artificial Intelligence and Statistics, Mai Khao, Thailand, 2025, vol.
258, pp. 118–126.
ista: 'Kalinin N, Steinberger L. 2025. Efficient estimation of a Gaussian mean with
local differential privacy. Proceedings of the 28th International Conference on
Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence
and Statistics, PMLR, vol. 258, 118–126.'
mla: Kalinin, Nikita, and Lukas Steinberger. “Efficient Estimation of a Gaussian
Mean with Local Differential Privacy.” Proceedings of the 28th International
Conference on Artificial Intelligence and Statistics, vol. 258, ML Research
Press, 2025, pp. 118–26.
short: N. Kalinin, L. Steinberger, in:, Proceedings of the 28th International Conference
on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 118–126.
conference:
end_date: 2025-05-05
location: Mai Khao, Thailand
name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
start_date: 2025-05-03
corr_author: '1'
date_created: 2025-09-07T22:01:34Z
date_published: 2025-05-01T00:00:00Z
date_updated: 2025-09-09T08:28:41Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
external_id:
arxiv:
- '2402.04840'
file:
- access_level: open_access
checksum: 3dcd59988ca974b98662ba09a516e616
content_type: application/pdf
creator: dernst
date_created: 2025-09-09T08:26:44Z
date_updated: 2025-09-09T08:26:44Z
file_id: '20316'
file_name: 2025_AISTATS_Kalinin.pdf
file_size: 395864
relation: main_file
success: 1
file_date_updated: 2025-09-09T08:26:44Z
has_accepted_license: '1'
intvolume: ' 258'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 118-126
publication: Proceedings of the 28th International Conference on Artificial Intelligence
and Statistics
publication_identifier:
eissn:
- 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient estimation of a Gaussian mean with local differential privacy
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 258
year: '2025'
...
---
OA_place: publisher
OA_type: gold
_id: '18875'
abstract:
- lang: eng
text: Current state-of-the-art methods for differentially private model training
are based on matrix factorization techniques. However, these methods suffer from
high computational overhead because they require numerically solving a demanding
optimization problem to determine an approximately optimal factorization prior
to the actual model training. In this work, we present a new matrix factorization
approach, BSR, which overcomes this computational bottleneck. By exploiting properties
of the standard matrix square root, BSR allows to efficiently handle also large-scale
problems. For the key scenario of stochastic gradient descent with momentum and
weight decay, we even derive analytical expressions for BSR that render the computational
overhead negligible. We prove bounds on the approximation quality that hold both
in the centralized and in the federated learning setting. Our numerical experiments
demonstrate that models trained using BSR perform on par with the best existing
methods, while completely avoiding their computational overhead.
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikita
full_name: Kalinin, Nikita
id: 4b14526e-14d2-11ed-ba64-c14c9553d137
last_name: Kalinin
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kalinin N, Lampert C. Banded square root matrix factorization for differentially
private model training. In: 38th Annual Conference on Neural Information Processing
Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.'
apa: 'Kalinin, N., & Lampert, C. (2024). Banded square root matrix factorization
for differentially private model training. In 38th Annual Conference on Neural
Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information
Processing Systems Foundation.'
chicago: Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization
for Differentially Private Model Training.” In 38th Annual Conference on Neural
Information Processing Systems, Vol. 37. Neural Information Processing Systems
Foundation, 2024.
ieee: N. Kalinin and C. Lampert, “Banded square root matrix factorization for differentially
private model training,” in 38th Annual Conference on Neural Information Processing
Systems, Vancouver, Canada, 2024, vol. 37.
ista: 'Kalinin N, Lampert C. 2024. Banded square root matrix factorization for differentially
private model training. 38th Annual Conference on Neural Information Processing
Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information
Processing Systems, vol. 37.'
mla: Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization
for Differentially Private Model Training.” 38th Annual Conference on Neural
Information Processing Systems, vol. 37, Neural Information Processing Systems
Foundation, 2024.
short: N. Kalinin, C. Lampert, in:, 38th Annual Conference on Neural Information
Processing Systems, Neural Information Processing Systems Foundation, 2024.
conference:
end_date: 2024-12-16
location: Vancouver, Canada
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2024-12-16
corr_author: '1'
date_created: 2025-01-24T17:58:16Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-05-14T11:34:20Z
day: '01'
ddc:
- '000'
department:
- _id: GradSch
- _id: ChLa
external_id:
arxiv:
- '2405.13763'
file:
- access_level: open_access
checksum: a216cab8eddc1fe7840aede0e2c0d41e
content_type: application/pdf
creator: dernst
date_created: 2025-01-27T09:52:15Z
date_updated: 2025-01-27T09:52:15Z
file_id: '18888'
file_name: 2024_NeurIPS_Nikita.pdf
file_size: 1144656
relation: main_file
success: 1
file_date_updated: 2025-01-27T09:52:15Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: 38th Annual Conference on Neural Information Processing Systems
publication_identifier:
eissn:
- 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: Banded square root matrix factorization for differentially private model training
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2024'
...