4 Search Results for "Cheu, Albert"

Document
DOI: 10.4230/LIPIcs.ITC.2024.4
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient

Authors: Badih Ghazi, Ravi Kumar, and Pasin Manurangsi

Published in: LIPIcs, Volume 304, 5th Conference on Information-Theoretic Cryptography (ITC 2024)

Abstract
We obtain a new protocol for binary counting in the ε-DP_shuffle model with error O(1/ε) and expected communication Õ((log n)/ε) messages per user. Previous protocols incur either an error of O(1/ε^1.5) with O_ε(log n) messages per user (Ghazi et al., ITC 2020) or an error of O(1/ε) with O_ε(n²) messages per user (Cheu and Yan, TPDP 2022). Using the new protocol, we obtained improved ε-DP_shuffle protocols for real summation and histograms.
Cite as

Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. In 5th Conference on Information-Theoretic Cryptography (ITC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 304, pp. 4:1-4:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{ghazi_et_al:LIPIcs.ITC.2024.4,
  author =	{Ghazi, Badih and Kumar, Ravi and Manurangsi, Pasin},
  title =	{{Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient}},
  booktitle =	{5th Conference on Information-Theoretic Cryptography (ITC 2024)},
  pages =	{4:1--4:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-333-1},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{304},
  editor =	{Aggarwal, Divesh},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2024.4},
  URN =		{urn:nbn:de:0030-drops-205127},
  doi =		{10.4230/LIPIcs.ITC.2024.4},
  annote =	{Keywords: Differential Privacy, Shuffle Model, Aggregation, Pure Differential Privacy}
}
Document
DOI: 10.4230/LIPIcs.ITC.2024.6
Information-Theoretic Single-Server PIR in the Shuffle Model

Authors: Yuval Ishai, Mahimna Kelkar, Daniel Lee, and Yiping Ma

Published in: LIPIcs, Volume 304, 5th Conference on Information-Theoretic Cryptography (ITC 2024)

Abstract
We revisit the problem of private information retrieval (PIR) in the shuffle model, where queries can be made anonymously by multiple clients. We present the first single-server PIR protocol in this model that has sublinear per-client communication and information-theoretic security. Moreover, following one-time preprocessing on the server side, our protocol only requires sublinear per-client computation. Concretely, for every γ > 0, the protocol has O(n^{γ}) communication and computation costs per (stateless) client, with 1/poly(n) statistical security, assuming that a size-n database is simultaneously accessed by poly(n) clients. This should be contrasted with the recent breakthrough result of Lin, Mook, and Wichs (STOC 2023) on doubly efficient PIR in the standard model, which is (inherently) limited to computational security.
Cite as

Yuval Ishai, Mahimna Kelkar, Daniel Lee, and Yiping Ma. Information-Theoretic Single-Server PIR in the Shuffle Model. In 5th Conference on Information-Theoretic Cryptography (ITC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 304, pp. 6:1-6:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{ishai_et_al:LIPIcs.ITC.2024.6,
  author =	{Ishai, Yuval and Kelkar, Mahimna and Lee, Daniel and Ma, Yiping},
  title =	{{Information-Theoretic Single-Server PIR in the Shuffle Model}},
  booktitle =	{5th Conference on Information-Theoretic Cryptography (ITC 2024)},
  pages =	{6:1--6:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-333-1},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{304},
  editor =	{Aggarwal, Divesh},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2024.6},
  URN =		{urn:nbn:de:0030-drops-205142},
  doi =		{10.4230/LIPIcs.ITC.2024.6},
  annote =	{Keywords: Private information retrieval, Shuffle model}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2023.36
Necessary Conditions in Multi-Server Differential Privacy

Authors: Albert Cheu and Chao Yan

Published in: LIPIcs, Volume 251, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023)

Abstract
We consider protocols where users communicate with multiple servers to perform a computation on the users' data. An adversary exerts semi-honest control over many of the parties but its view is differentially private with respect to honest users. Prior work described protocols that required multiple rounds of interaction or offered privacy against a computationally bounded adversary. Our work presents limitations of non-interactive protocols that offer privacy against unbounded adversaries. We prove that these protocols require exponentially more samples than centrally private counterparts to solve some learning, testing, and estimation tasks. This means sample-efficiency demands interactivity or computational differential privacy, or both.
Cite as

Albert Cheu and Chao Yan. Necessary Conditions in Multi-Server Differential Privacy. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 36:1-36:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{cheu_et_al:LIPIcs.ITCS.2023.36,
  author =	{Cheu, Albert and Yan, Chao},
  title =	{{Necessary Conditions in Multi-Server Differential Privacy}},
  booktitle =	{14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
  pages =	{36:1--36:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-263-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{251},
  editor =	{Tauman Kalai, Yael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.36},
  URN =		{urn:nbn:de:0030-drops-175395},
  doi =		{10.4230/LIPIcs.ITCS.2023.36},
  annote =	{Keywords: Differential Privacy, Parity Learning, Multi-server}
}
Document
DOI: 10.4230/LIPIcs.ITC.2020.1
Separating Local & Shuffled Differential Privacy via Histograms

Authors: Victor Balcer and Albert Cheu

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

Abstract
Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This implies an arbitrarily large gap in sample complexity between the shuffled and local models. On the other hand, we show that the models are equivalent when we impose the constraints of pure differential privacy and single-message randomizers.
Cite as

Victor Balcer and Albert Cheu. Separating Local & Shuffled Differential Privacy via Histograms. In 1st Conference on Information-Theoretic Cryptography (ITC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 163, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{balcer_et_al:LIPIcs.ITC.2020.1,
  author =	{Balcer, Victor and Cheu, Albert},
  title =	{{Separating Local \& Shuffled Differential Privacy via Histograms}},
  booktitle =	{1st Conference on Information-Theoretic Cryptography (ITC 2020)},
  pages =	{1:1--1:14},
  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.2020.1},
  URN =		{urn:nbn:de:0030-drops-121068},
  doi =		{10.4230/LIPIcs.ITC.2020.1},
  annote =	{Keywords: Differential Privacy, Distributed Protocols, Histograms}
}
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