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Documents authored by Cohen, Aloni

Document
DOI: 10.4230/LIPIcs.FORC.2026.17
Protecting the Undeleted in Machine Unlearning

Authors: Aloni Cohen, Refael Kohen, Kobbi Nissim, and Uri Stemmer

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
Machine unlearning aims to remove specific data points from a trained model, often striving to emulate "perfect retraining", i.e., producing the model that would have been obtained had the deleted data never been included. We demonstrate that this approach, and security definitions that enable it, carry significant privacy risks for the remaining (undeleted) data points. We present a reconstruction attack showing that for certain tasks, which can be computed securely without deletions, a mechanism adhering to perfect retraining allows an adversary controlling merely ω(1) data points to reconstruct almost the entire dataset simply by issuing deletion requests. We survey existing definitions for machine unlearning, showing they are either susceptible to such attacks or too restrictive to support basic functionalities like exact summation. To address this problem, we propose a new security definition that specifically safeguards undeleted data against leakage caused by the deletion of other points. We show that our definition permits several essential functionalities, such as bulletin boards, summations, and statistical learning.
Cite as

Aloni Cohen, Refael Kohen, Kobbi Nissim, and Uri Stemmer. Protecting the Undeleted in Machine Unlearning. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 17:1-17:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cohen_et_al:LIPIcs.FORC.2026.17,
  author =	{Cohen, Aloni and Kohen, Refael and Nissim, Kobbi and Stemmer, Uri},
  title =	{{Protecting the Undeleted in Machine Unlearning}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{17:1--17:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.17},
  URN =		{urn:nbn:de:0030-drops-259901},
  doi =		{10.4230/LIPIcs.FORC.2026.17},
  annote =	{Keywords: Unlearning, data deletion, privacy}
}
Document
DOI: 10.4230/LIPIcs.FORC.2026.19
A Machine Learning Theory Perspective on Strategic Litigation

Authors: Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may have far-reaching impact is by establishing new legal precedent that later courts must follow. In this paper, we explore strategic litigation from the perspective of machine learning theory. We consider an abstract model of a common law legal system where a lower court decides new cases by applying a decision rule learned from a higher court’s past rulings. In this model, we explore the power of a strategic litigator, who strategically brings cases to the higher court to influence the decision rule applied by the lower court in future cases. We explore questions including: What impact can a strategic litigator have? Which cases should a strategic litigator bring to court? Does it ever make sense for a strategic litigator to bring a case when they are sure the court will rule against them? We show that this strategic case selection problem has interesting structure, with even simple settings exhibiting counterintuitive phenomena. When cases are represented by points in one dimension and the lower court’s learning algorithm is nearest neighbor, or as points in d dimensions and the lower court’s learning algorithm is a support vector machine, we characterize the set of inducible decision rules and develop algorithms for selecting an optimal set of cases to bring to the higher court given the strategic litigator’s objectives.
Cite as

Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen. A Machine Learning Theory Perspective on Strategic Litigation. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{dutz_et_al:LIPIcs.FORC.2026.19,
  author =	{Dutz, Melissa and Shao, Han and Blum, Avrim and Cohen, Aloni},
  title =	{{A Machine Learning Theory Perspective on Strategic Litigation}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{19:1--19:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.19},
  URN =		{urn:nbn:de:0030-drops-259921},
  doi =		{10.4230/LIPIcs.FORC.2026.19},
  annote =	{Keywords: Strategic Litigation, Machine Learning Theory, Law}
}
Document
DOI: 10.4230/LIPIcs.FORC.2021.5
Census TopDown: The Impacts of Differential Privacy on Redistricting

Authors: Aloni Cohen, Moon Duchin, JN Matthews, and Bhushan Suwal

Published in: LIPIcs, Volume 192, 2nd Symposium on Foundations of Responsible Computing (FORC 2021)

Abstract
The 2020 Decennial Census will be released with a new disclosure avoidance system in place, putting differential privacy in the spotlight for a wide range of data users. We consider several key applications of Census data in redistricting, developing tools and demonstrations for practitioners who are concerned about the impacts of this new noising algorithm called TopDown. Based on a close look at reconstructed Texas data, we find reassuring evidence that TopDown will not threaten the ability to produce districts with tolerable population balance or to detect signals of racial polarization for Voting Rights Act enforcement.
Cite as

Aloni Cohen, Moon Duchin, JN Matthews, and Bhushan Suwal. Census TopDown: The Impacts of Differential Privacy on Redistricting. In 2nd Symposium on Foundations of Responsible Computing (FORC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 192, pp. 5:1-5:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{cohen_et_al:LIPIcs.FORC.2021.5,
  author =	{Cohen, Aloni and Duchin, Moon and Matthews, JN and Suwal, Bhushan},
  title =	{{Census TopDown: The Impacts of Differential Privacy on Redistricting}},
  booktitle =	{2nd Symposium on Foundations of Responsible Computing (FORC 2021)},
  pages =	{5:1--5:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-187-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{192},
  editor =	{Ligett, Katrina and Gupta, Swati},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2021.5},
  URN =		{urn:nbn:de:0030-drops-138736},
  doi =		{10.4230/LIPIcs.FORC.2021.5},
  annote =	{Keywords: Census, TopDown, differential privacy, redistricting, Voting Rights Act}
}
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