3 Search Results for "Amos-Binks, Adam"

Document
DOI: 10.4230/LIPIcs.ITC.2020.14
The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers

Authors: Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, and Uri Stemmer

Published in: LIPIcs, Volume 163, 1st Conference on Information-Theoretic Cryptography (ITC 2020)

Abstract
Motivated by the desire to bridge the utility gap between local and trusted curator models of differential privacy for practical applications, we initiate the theoretical study of a hybrid model introduced by "Blender" [Avent et al., USENIX Security '17], in which differentially private protocols of n agents that work in the local-model are assisted by a differentially private curator that has access to the data of m additional users. We focus on the regime where m ≪ n and study the new capabilities of this (m,n)-hybrid model. We show that, despite the fact that the hybrid model adds no significant new capabilities for the basic task of simple hypothesis-testing, there are many other tasks (under a wide range of parameters) that can be solved in the hybrid model yet cannot be solved either by the curator or by the local-users separately. Moreover, we exhibit additional tasks where at least one round of interaction between the curator and the local-users is necessary - namely, no hybrid model protocol without such interaction can solve these tasks. Taken together, our results show that the combination of the local model with a small curator can become part of a promising toolkit for designing and implementing differential privacy.
Cite as

Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, and Uri Stemmer. The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. In 1st Conference on Information-Theoretic Cryptography (ITC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 163, pp. 14:1-14:25, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)

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@InProceedings{beimel_et_al:LIPIcs.ITC.2020.14,
  author =	{Beimel, Amos and Korolova, Aleksandra and Nissim, Kobbi and Sheffet, Or and Stemmer, Uri},
  title =	{{The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers}},
  booktitle =	{1st Conference on Information-Theoretic Cryptography (ITC 2020)},
  pages =	{14:1--14:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-151-1},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{163},
  editor =	{Tauman Kalai, Yael and Smith, Adam D. and Wichs, Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2020.14},
  URN =		{urn:nbn:de:0030-drops-121195},
  doi =		{10.4230/LIPIcs.ITC.2020.14},
  annote =	{Keywords: differential privacy, hybrid model, private learning, local model}
}
Document
DOI: 10.4230/OASIcs.CMN.2016.9
Summarizing and Comparing Story Plans

Authors: Adam Amos-Binks, David L. Roberts, and R. Michael Young

Published in: OASIcs, Volume 53, 7th Workshop on Computational Models of Narrative (CMN 2016)

Abstract
Branching story games have gained popularity for creating unique playing experiences by adapting story content in response to user actions. Research in interactive narrative (IN) uses automated planning to generate story plans for a given story problem. However, a story planner can generate multiple story plan solutions, all of which equally-satisfy the story problem definition but contain different story content. These differences in story content are key to understanding the story branches in a story problem's solution space, however we lack narrative-theoretic metrics to compare story plans. We address this gap by first defining a story plan summarization model to capture the important story semantics from a story plan. Secondly, we define a story plan comparison metric that compares story plans based on the summarization model. Using the Glaive narrative planner and a simple story problem, we demonstrate the usefulness of using the summarization model and distance metric to characterize the different story branches in a story problem's solution space.
Cite as

Adam Amos-Binks, David L. Roberts, and R. Michael Young. Summarizing and Comparing Story Plans. In 7th Workshop on Computational Models of Narrative (CMN 2016). Open Access Series in Informatics (OASIcs), Volume 53, pp. 9:1-9:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)

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@InProceedings{amosbinks_et_al:OASIcs.CMN.2016.9,
  author =	{Amos-Binks, Adam and Roberts, David L. and Young, R. Michael},
  title =	{{Summarizing and Comparing Story Plans}},
  booktitle =	{7th Workshop on Computational Models of Narrative (CMN 2016)},
  pages =	{9:1--9:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-020-0},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{53},
  editor =	{Miller, Ben and Lieto, Antonio and Ronfard, R\'{e}mi and Ware, Stephen G. and Finlayson, Mark A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.CMN.2016.9},
  URN =		{urn:nbn:de:0030-drops-67100},
  doi =		{10.4230/OASIcs.CMN.2016.9},
  annote =	{Keywords: artifical intelligence, planning, narrative, comparison, story}
}
Document
DOI: 10.4230/OASIcs.CMN.2015.152
Good Timing for Computational Models of Narrative Discourse

Authors: David R. Winer, Adam A. Amos-Binks, Camille Barot, and R. Michael Young

Published in: OASIcs, Volume 45, 6th Workshop on Computational Models of Narrative (CMN 2015)

Abstract
The temporal order in which story events are presented in discourse can greatly impact how readers experience narrative; however, it remains unclear how narrative systems can leverage temporal order to affect comprehension and experience. We define structural properties of discourse which provide a basis for computational narratologists to reason about good timing, such as when readers learn about event relationships.
Cite as

David R. Winer, Adam A. Amos-Binks, Camille Barot, and R. Michael Young. Good Timing for Computational Models of Narrative Discourse. In 6th Workshop on Computational Models of Narrative (CMN 2015). Open Access Series in Informatics (OASIcs), Volume 45, pp. 152-156, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2015)

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@InProceedings{winer_et_al:OASIcs.CMN.2015.152,
  author =	{Winer, David R. and Amos-Binks, Adam A. and Barot, Camille and Young, R. Michael},
  title =	{{Good Timing for Computational Models of Narrative Discourse}},
  booktitle =	{6th Workshop on Computational Models of Narrative (CMN 2015)},
  pages =	{152--156},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-93-4},
  ISSN =	{2190-6807},
  year =	{2015},
  volume =	{45},
  editor =	{Finlayson, Mark A. and Miller, Ben and Lieto, Antonio and Ronfard, Remi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.CMN.2015.152},
  URN =		{urn:nbn:de:0030-drops-52897},
  doi =		{10.4230/OASIcs.CMN.2015.152},
  annote =	{Keywords: causal inference, narrative, discourse structure, computational model}
}
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