4 Search Results for "Alvim, Mário S."

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/OASIcs.Gabbrielli.1
Locally Static, Globally Dynamic Session Types for Active Objects

Authors: Reiner Hähnle, Anton W. Haubner, and Eduard Kamburjan

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract
Active object languages offer an attractive trade-off between low-level, preemptive concurrency and fully distributed actors: syntactically identifiable atomic code segments and asynchronous calls are the basis of cooperative concurrency, still permitting interleaving, but nevertheless being mechanically analyzable. The challenge is to reconcile local static analysis of atomic segments with the global scheduling constraints it depends on. Here, we propose an approximate, hybrid approach; At compile-time we perform a local static analysis: later, any run not complying to a global specification is excluded via runtime checks. That specification is expressed in a type-theoretic language inspired by session types. The approach reverses the usual (first global, then local) order of analysis and, thereby, supports analysis of open distributed systems.
Cite as

Reiner Hähnle, Anton W. Haubner, and Eduard Kamburjan. Locally Static, Globally Dynamic Session Types for Active Objects. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 1:1-1:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{hahnle_et_al:OASIcs.Gabbrielli.1,
  author =	{H\"{a}hnle, Reiner and Haubner, Anton W. and Kamburjan, Eduard},
  title =	{{Locally Static, Globally Dynamic Session Types for Active Objects}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{1:1--1:24},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.1},
  URN =		{urn:nbn:de:0030-drops-132237},
  doi =		{10.4230/OASIcs.Gabbrielli.1},
  annote =	{Keywords: Session Types, Active Objects, Runtime Verification, Static Verification}
}
Document
DOI: 10.4230/OASIcs.Gabbrielli.11
Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Authors: Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract
This paper shows how we can combine the power of machine learning with the flexibility of constraints. More specifically, we show how machine learning models can be represented by first-order logic theories, and how to derive these theories. The advantage of this representation is that it can be augmented with additional formulae, representing constraints of some kind on the data domain. For instance, new knowledge, or potential attackers, or fairness desiderata. We consider various kinds of learning algorithms (neural networks, k-nearest-neighbours, decision trees, support vector machines) and for each of them we show how to infer the FOL formulae. Then we focus on one particular application domain, namely the field of security and privacy. The idea is to represent the potentialities and goals of the attacker as a set of constraints, then use a constraint solver (more precisely, a solver modulo theories) to verify the satisfiability. If a solution exists, then it means that an attack is possible, otherwise, the system is safe. We show various examples from different areas of security and privacy; specifically, we consider a side-channel attack on a password checker, a malware attack on smart health systems, and a model-inversion attack on a neural network.
Cite as

Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli. Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{falaschi_et_al:OASIcs.Gabbrielli.11,
  author =	{Falaschi, Moreno and Palamidessi, Catuscia and Romanelli, Marco},
  title =	{{Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{11:1--11:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.11},
  URN =		{urn:nbn:de:0030-drops-132338},
  doi =		{10.4230/OASIcs.Gabbrielli.11},
  annote =	{Keywords: Constraints, machine learning, privacy, security}
}
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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