5 Search Results for "Fernandes, Natasha"

Document
DOI: 10.4230/LIPIcs.FORC.2025.8
Differential Privacy Under Multiple Selections

Authors: Ashish Goel, Zhihao Jiang, Aleksandra Korolova, Kamesh Munagala, and Sahasrajit Sarmasarkar

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
We consider the setting where a user with sensitive features wishes to obtain a recommendation from a server in a differentially private fashion. We propose a "multi-selection" architecture where the server can send back multiple recommendations and the user chooses one from these that matches best with their private features. When the user feature is one-dimensional - on an infinite line - and the accuracy measure is defined w.r.t some increasing function 𝔥(.) of the distance on the line, we precisely characterize the optimal mechanism that satisfies differential privacy. The specification of the optimal mechanism includes both the distribution of the noise that the user adds to its private value, and the algorithm used by the server to determine the set of results to send back as a response. We show that Laplace is an optimal noise distribution in this setting. Furthermore, we show that this optimal mechanism results in an error that is inversely proportional to the number of results returned when the function 𝔥(.) is the identity function.
Cite as

Ashish Goel, Zhihao Jiang, Aleksandra Korolova, Kamesh Munagala, and Sahasrajit Sarmasarkar. Differential Privacy Under Multiple Selections. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 8:1-8:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{goel_et_al:LIPIcs.FORC.2025.8,
  author =	{Goel, Ashish and Jiang, Zhihao and Korolova, Aleksandra and Munagala, Kamesh and Sarmasarkar, Sahasrajit},
  title =	{{Differential Privacy Under Multiple Selections}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{8:1--8:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.8},
  URN =		{urn:nbn:de:0030-drops-231353},
  doi =		{10.4230/LIPIcs.FORC.2025.8},
  annote =	{Keywords: Differential Privacy, Mechanism Design and Multi-Selection}
}
Document
DOI: 10.4230/LIPIcs.FORC.2025.6
Near-Universally-Optimal Differentially Private Minimum Spanning Trees

Authors: Richard Hladík and Jakub Tětek

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
Devising mechanisms with good beyond-worst-case input-dependent performance has been an important focus of differential privacy, with techniques such as smooth sensitivity, propose-test-release, or inverse sensitivity mechanism being developed to achieve this goal. This makes it very natural to use the notion of universal optimality in differential privacy. Universal optimality is a strong instance-specific optimality guarantee for problems on weighted graphs, which roughly states that for any fixed underlying (unweighted) graph, the algorithm is optimal in the worst-case sense, with respect to the possible setting of the edge weights. In this paper, we give the first such result in differential privacy. Namely, we prove that a simple differentially private mechanism for approximately releasing the minimum spanning tree is near-optimal in the sense of universal optimality for the 𝓁₁ neighbor relation. Previously, it was only known that this mechanism is nearly optimal in the worst case. We then focus on the 𝓁_∞ neighbor relation, for which the described mechanism is not optimal. We show that one may implement the exponential mechanism for MST in polynomial time, and that this results in universal near-optimality for both the 𝓁₁ and the 𝓁_∞ neighbor relations.
Cite as

Richard Hladík and Jakub Tětek. Near-Universally-Optimal Differentially Private Minimum Spanning Trees. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 6:1-6:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hladik_et_al:LIPIcs.FORC.2025.6,
  author =	{Hlad{\'\i}k, Richard and T\v{e}tek, Jakub},
  title =	{{Near-Universally-Optimal Differentially Private Minimum Spanning Trees}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{6:1--6:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.6},
  URN =		{urn:nbn:de:0030-drops-231337},
  doi =		{10.4230/LIPIcs.FORC.2025.6},
  annote =	{Keywords: differential privacy, universal optimality, minimum spanning trees}
}
Document
DOI: 10.4230/LIPIcs.FORC.2025.2
Smooth Sensitivity Revisited: Towards Optimality

Authors: Richard Hladík and Jakub Tětek

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
Smooth sensitivity is one of the most commonly used techniques for designing practical differentially private mechanisms. In this approach, one computes the smooth sensitivity of a given query q on the given input D and releases q(D) with noise added proportional to this smooth sensitivity. One question remains: what distribution should we pick the noise from? In this paper, we give a new class of distributions suitable for the use with smooth sensitivity, which we name the PolyPlace distribution. This distribution improves upon the state-of-the-art Student’s T distribution in terms of standard deviation by arbitrarily large factors, depending on a "smoothness parameter" γ, which one has to set in the smooth sensitivity framework. Moreover, our distribution is defined for a wider range of parameter γ, which can lead to significantly better performance. Furthermore, we prove that the PolyPlace distribution converges for γ → 0 to the Laplace distribution and so does its variance. This means that the Laplace mechanism is a limit special case of the PolyPlace mechanism. This implies that our mechanism is in a certain sense optimal for γ → 0.
Cite as

Richard Hladík and Jakub Tětek. Smooth Sensitivity Revisited: Towards Optimality. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 2:1-2:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hladik_et_al:LIPIcs.FORC.2025.2,
  author =	{Hlad{\'\i}k, Richard and T\v{e}tek, Jakub},
  title =	{{Smooth Sensitivity Revisited: Towards Optimality}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{2:1--2:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.2},
  URN =		{urn:nbn:de:0030-drops-231292},
  doi =		{10.4230/LIPIcs.FORC.2025.2},
  annote =	{Keywords: differential privacy, smooth sensitivity}
}
Document
Invited Talk
DOI: 10.4230/LIPIcs.CSL.2022.2
How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk)

Authors: Natasha Fernandes, Annabelle McIver, and Carroll Morgan

Published in: LIPIcs, Volume 216, 30th EACSL Annual Conference on Computer Science Logic (CSL 2022)

Abstract
The study of quantitative risk in security systems is often based around complex and subtle mathematical ideas involving probabilities. The notations for these ideas can pose a communication barrier between collaborating researchers even when those researchers are working within a similar framework. This paper describes the use of geometrical representation and reasoning as a way to share ideas using the minimum of notation so as to build intuition about what kinds of properties might or might not be true. We describe a faithful geometrical setting for the channel model of quantitative information flow (QIF) and demonstrate how it can facilitate "proofs without words" for problems in the QIF setting.
Cite as

Natasha Fernandes, Annabelle McIver, and Carroll Morgan. How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk). In 30th EACSL Annual Conference on Computer Science Logic (CSL 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 216, pp. 2:1-2:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{fernandes_et_al:LIPIcs.CSL.2022.2,
  author =	{Fernandes, Natasha and McIver, Annabelle and Morgan, Carroll},
  title =	{{How to Develop an Intuition for Risk... and Other Invisible Phenomena}},
  booktitle =	{30th EACSL Annual Conference on Computer Science Logic (CSL 2022)},
  pages =	{2:1--2:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-218-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{216},
  editor =	{Manea, Florin and Simpson, Alex},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CSL.2022.2},
  URN =		{urn:nbn:de:0030-drops-157227},
  doi =		{10.4230/LIPIcs.CSL.2022.2},
  annote =	{Keywords: Geometry, Quantitative Information Flow, Proof, Explainability, Privacy}
}
Document
Invited Paper
DOI: 10.4230/LIPIcs.CONCUR.2020.1
On Privacy and Accuracy in Data Releases (Invited Paper)

Authors: Mário S. Alvim, Natasha Fernandes, Annabelle McIver, and Gabriel H. Nunes

Published in: LIPIcs, Volume 171, 31st International Conference on Concurrency Theory (CONCUR 2020)

Abstract
In this paper we study the relationship between privacy and accuracy in the context of correlated datasets. We use a model of quantitative information flow to describe the the trade-off between privacy of individuals' data and and the utility of queries to that data by modelling the effectiveness of adversaries attempting to make inferences after a data release. We show that, where correlations exist in datasets, it is not possible to implement optimal noise-adding mechanisms that give the best possible accuracy or the best possible privacy in all situations. Finally we illustrate the trade-off between accuracy and privacy for local and oblivious differentially private mechanisms in terms of inference attacks on medium-scale datasets.
Cite as

Mário S. Alvim, Natasha Fernandes, Annabelle McIver, and Gabriel H. Nunes. On Privacy and Accuracy in Data Releases (Invited Paper). In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 1:1-1:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{alvim_et_al:LIPIcs.CONCUR.2020.1,
  author =	{Alvim, M\'{a}rio S. and Fernandes, Natasha and McIver, Annabelle and Nunes, Gabriel H.},
  title =	{{On Privacy and Accuracy in Data Releases}},
  booktitle =	{31st International Conference on Concurrency Theory (CONCUR 2020)},
  pages =	{1:1--1:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-160-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{171},
  editor =	{Konnov, Igor and Kov\'{a}cs, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2020.1},
  URN =		{urn:nbn:de:0030-drops-128130},
  doi =		{10.4230/LIPIcs.CONCUR.2020.1},
  annote =	{Keywords: Privacy/utility trade-off, Quantitative Information Flow, inference attacks}
}
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