2020 Knuth Prize Is Awarded to Cynthia Dwork
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Going Beyond Dual Execution: MPC for Functions with Efficient Verification
Going Beyond Dual Execution: MPC for Functions with Efficient Verification Carmit Hazay abhi shelat ∗ Bar-Ilan University Northeastern Universityy Muthuramakrishnan Venkitasubramaniamz University of Rochester Abstract The dual execution paradigm of Mohassel and Franklin (PKC’06) and Huang, Katz and Evans (IEEE ’12) shows how to achieve the notion of 1-bit leakage security at roughly twice the cost of semi-honest security for the special case of two-party secure computation. To date, there are no multi-party compu- tation (MPC) protocols that offer such a strong trade-off between security and semi-honest performance. Our main result is to address this shortcoming by designing 1-bit leakage protocols for the multi- party setting, albeit for a special class of functions. We say that function f (x, y) is efficiently verifiable by g if the running time of g is always smaller than f and g(x, y, z) = 1 if and only if f (x, y) = z. In the two-party setting, we first improve dual execution by observing that the “second execution” can be an evaluation of g instead of f , and that by definition, the evaluation of g is asymptotically more efficient. Our main MPC result is to construct a 1-bit leakage protocol for such functions from any passive protocol for f that is secure up to additive errors and any active protocol for g. An important result by Genkin et al. (STOC ’14) shows how the classic protocols by Goldreich et al. (STOC ’87) and Ben-Or et al. (STOC ’88) naturally support this property, which allows to instantiate our compiler with two-party and multi-party protocols. -
Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing 234 Able Quantity
Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing 234 able quantity. Only a limited number of previous works which can provide a natural and interpretable guide- [29, 34, 35] have investigated the question of how to line for selecting proper privacy parameters by system select a proper value of ǫ, but these approaches ei- designers and researchers. Furthermore, we extend our ther require complicated economic models or lack the analysis on unbounded DP to bounded DP and the ap- analysis of adversaries with arbitrary auxiliary informa- proximate (ǫ, δ)-DP. tion (see Section 2.3 for more details). Our work is in- Impact of Auxiliary Information. The conjecture spired by the interpretation of differential privacy via that auxiliary information can influence the design of hypothesis testing, initially introduced by Wasserman DP mechanisms has been made in prior work [8, 28, 32, and Zhou [27, 30, 60]. However, this interpretation has 33, 39, 65]. We therefore investigate the adversary’s ca- not been systematically investigated before in the con- pability based on hypothesis testing under three types text of our research objective, i.e., reasoning about the of auxiliary information: the prior distribution of the in- choice of the privacy parameter ǫ (see Section 2.4 for put record, the correlation across records, and the corre- more details). lation across time. Our analysis demonstrates that the We consider hypothesis testing [2, 45, 61] as the tool auxiliary information indeed influences the appropriate used by the adversary to infer sensitive information of selection of ǫ. The results suggest that, when possible an individual record (e.g., the presence or absence of and available, the practitioners of DP should explicitly a record in the database for unbounded DP) from the incorporate adversary’s auxiliary information into the outputs of privacy mechanisms. -
2008 Annual Report
2008 Annual Report NATIONAL ACADEMY OF ENGINEERING ENGINEERING THE FUTURE 1 Letter from the President 3 In Service to the Nation 3 Mission Statement 4 Program Reports 4 Engineering Education 4 Center for the Advancement of Scholarship on Engineering Education 6 Technological Literacy 6 Public Understanding of Engineering Developing Effective Messages Media Relations Public Relations Grand Challenges for Engineering 8 Center for Engineering, Ethics, and Society 9 Diversity in the Engineering Workforce Engineer Girl! Website Engineer Your Life Project Engineering Equity Extension Service 10 Frontiers of Engineering Armstrong Endowment for Young Engineers-Gilbreth Lectures 12 Engineering and Health Care 14 Technology and Peace Building 14 Technology for a Quieter America 15 America’s Energy Future 16 Terrorism and the Electric Power-Delivery System 16 U.S.-China Cooperation on Electricity from Renewables 17 U.S.-China Symposium on Science and Technology Strategic Policy 17 Offshoring of Engineering 18 Gathering Storm Still Frames the Policy Debate 20 2008 NAE Awards Recipients 22 2008 New Members and Foreign Associates 24 2008 NAE Anniversary Members 28 2008 Private Contributions 28 Einstein Society 28 Heritage Society 29 Golden Bridge Society 29 Catalyst Society 30 Rosette Society 30 Challenge Society 30 Charter Society 31 Other Individual Donors 34 The Presidents’ Circle 34 Corporations, Foundations, and Other Organizations 35 National Academy of Engineering Fund Financial Report 37 Report of Independent Certified Public Accountants 41 Notes to Financial Statements 53 Officers 53 Councillors 54 Staff 54 NAE Publications Letter from the President Engineering is critical to meeting the fundamental challenges facing the U.S. economy in the 21st century. -
Digital Communication Systems 2.2 Optimal Source Coding
Digital Communication Systems EES 452 Asst. Prof. Dr. Prapun Suksompong [email protected] 2. Source Coding 2.2 Optimal Source Coding: Huffman Coding: Origin, Recipe, MATLAB Implementation 1 Examples of Prefix Codes Nonsingular Fixed-Length Code Shannon–Fano code Huffman Code 2 Prof. Robert Fano (1917-2016) Shannon Award (1976 ) Shannon–Fano Code Proposed in Shannon’s “A Mathematical Theory of Communication” in 1948 The method was attributed to Fano, who later published it as a technical report. Fano, R.M. (1949). “The transmission of information”. Technical Report No. 65. Cambridge (Mass.), USA: Research Laboratory of Electronics at MIT. Should not be confused with Shannon coding, the coding method used to prove Shannon's noiseless coding theorem, or with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding. 3 Claude E. Shannon Award Claude E. Shannon (1972) Elwyn R. Berlekamp (1993) Sergio Verdu (2007) David S. Slepian (1974) Aaron D. Wyner (1994) Robert M. Gray (2008) Robert M. Fano (1976) G. David Forney, Jr. (1995) Jorma Rissanen (2009) Peter Elias (1977) Imre Csiszár (1996) Te Sun Han (2010) Mark S. Pinsker (1978) Jacob Ziv (1997) Shlomo Shamai (Shitz) (2011) Jacob Wolfowitz (1979) Neil J. A. Sloane (1998) Abbas El Gamal (2012) W. Wesley Peterson (1981) Tadao Kasami (1999) Katalin Marton (2013) Irving S. Reed (1982) Thomas Kailath (2000) János Körner (2014) Robert G. Gallager (1983) Jack KeilWolf (2001) Arthur Robert Calderbank (2015) Solomon W. Golomb (1985) Toby Berger (2002) Alexander S. Holevo (2016) William L. Root (1986) Lloyd R. Welch (2003) David Tse (2017) James L. -
László Lovász Avi Wigderson De L’Université Eötvös Loránd À De L’Institute for Advanced Study De Budapest, En Hongrie Et À Princeton, Aux États-Unis
2021 L’Académie des sciences et des lettres de Norvège a décidé de décerner le prix Abel 2021 à László Lovász Avi Wigderson de l’université Eötvös Loránd à de l’Institute for Advanced Study de Budapest, en Hongrie et à Princeton, aux États-Unis, « pour leurs contributions fondamentales à l’informatique théorique et aux mathématiques discrètes, et pour leur rôle de premier plan dans leur transformation en domaines centraux des mathématiques contemporaines ». L’informatique théorique est l’étude de la puissance croissante sur plusieurs autres sciences, ce et des limites du calcul. Elle trouve son origine qui permet de faire de nouvelles découvertes dans les travaux fondateurs de Kurt Gödel, Alonzo en « chaussant des lunettes d’informaticien ». Church, Alan Turing et John von Neumann, qui ont Les structures discrètes telles que les graphes, conduit au développement de véritables ordinateurs les chaînes de caractères et les permutations physiques. L’informatique théorique comprend deux sont au cœur de l’informatique théorique, et les sous-disciplines complémentaires : l’algorithmique mathématiques discrètes et l’informatique théorique qui développe des méthodes efficaces pour une ont naturellement été des domaines étroitement multitude de problèmes de calcul ; et la complexité, liés. Certes, ces deux domaines ont énormément qui montre les limites inhérentes à l’efficacité des bénéficié des champs de recherche plus traditionnels algorithmes. La notion d’algorithmes en temps des mathématiques, mais on constate également polynomial mise en avant dans les années 1960 par une influence croissante dans le sens inverse. Alan Cobham, Jack Edmonds et d’autres, ainsi que Les applications, concepts et techniques de la célèbre conjecture P≠NP de Stephen Cook, Leonid l’informatique théorique ont généré de nouveaux Levin et Richard Karp ont eu un fort impact sur le défis, ouvert de nouvelles directions de recherche domaine et sur les travaux de Lovász et Wigderson. -
An Economic Analysis of Privacy Protection and Statistical Accuracy As Social Choices
An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices John M. Abowd and Ian M. Schmutte August 15, 2018 Forthcoming in American Economic Review Abowd: U.S. Census Bureau HQ 8H120, 4600 Silver Hill Rd., Washington, DC 20233, and Cornell University, (email: [email protected]); Schmutte: Department of Eco- nomics, University of Georgia, B408 Amos Hall, Athens, GA 30602 (email: [email protected]). Abowd and Schmutte acknowledge the support of Alfred P. Sloan Foundation Grant G-2015-13903 and NSF Grant SES-1131848. Abowd acknowledges direct support from NSF Grants BCS-0941226, TC-1012593. Any opinions and conclusions are those of the authors and do not represent the views of the Census Bureau, NSF, or the Sloan Foundation. We thank the Center for Labor Economics at UC–Berkeley and Isaac Newton Institute for Mathematical Sciences, Cambridge (EPSRC grant no. EP/K032208/1) for support and hospitality. We are extremely grateful for very valuable com- ments and guidance from the editor, Pinelopi Goldberg, and six anonymous referees. We acknowl- edge helpful comments from Robin Bachman, Nick Bloom, Larry Blume, David Card, Michael Castro, Jennifer Childs, Melissa Creech, Cynthia Dwork, Casey Eggleston, John Eltinge, Stephen Fienberg, Mark Kutzbach, Ron Jarmin, Christa Jones, Dan Kifer, Ashwin Machanavajjhala, Frank McSherry, Gerome Miklau, Kobbi Nissim, Paul Oyer, Mallesh Pai, Jerry Reiter, Eric Slud, Adam Smith, Bruce Spencer, Sara Sullivan, Salil Vadhan, Lars Vilhuber, Glen Weyl, and Nellie Zhao along with seminar and conference participants at the U.S. Census Bureau, Cornell, CREST, George Ma- son, Georgetown, Microsoft Research–NYC, University of Washington Evans School, and SOLE. -
The Limits of Post-Selection Generalization
The Limits of Post-Selection Generalization Kobbi Nissim∗ Adam Smithy Thomas Steinke Georgetown University Boston University IBM Research – Almaden [email protected] [email protected] [email protected] Uri Stemmerz Jonathan Ullmanx Ben-Gurion University Northeastern University [email protected] [email protected] Abstract While statistics and machine learning offers numerous methods for ensuring gener- alization, these methods often fail in the presence of post selection—the common practice in which the choice of analysis depends on previous interactions with the same dataset. A recent line of work has introduced powerful, general purpose algorithms that ensure a property called post hoc generalization (Cummings et al., COLT’16), which says that no person when given the output of the algorithm should be able to find any statistic for which the data differs significantly from the population it came from. In this work we show several limitations on the power of algorithms satisfying post hoc generalization. First, we show a tight lower bound on the error of any algorithm that satisfies post hoc generalization and answers adaptively chosen statistical queries, showing a strong barrier to progress in post selection data analysis. Second, we show that post hoc generalization is not closed under composition, despite many examples of such algorithms exhibiting strong composition properties. 1 Introduction Consider a dataset X consisting of n independent samples from some unknown population P. How can we ensure that the conclusions drawn from X generalize to the population P? Despite decades of research in statistics and machine learning on methods for ensuring generalization, there is an increased recognition that many scientific findings do not generalize, with some even declaring this to be a “statistical crisis in science” [14]. -
Individuals and Privacy in the Eye of Data Analysis
Individuals and Privacy in the Eye of Data Analysis Thesis submitted in partial fulfillment of the requirements for the degree of “DOCTOR OF PHILOSOPHY” by Uri Stemmer Submitted to the Senate of Ben-Gurion University of the Negev October 2016 Beer-Sheva This work was carried out under the supervision of Prof. Amos Beimel and Prof. Kobbi Nissim In the Department of Computer Science Faculty of Natural Sciences Acknowledgments I could not have asked for better advisors. I will be forever grateful for their close guidance, their constant encouragement, and the warm shelter they provided. Without them, this thesis could have never begun. I have been fortunate to work with Raef Bassily, Amos Beimel, Mark Bun, Kobbi Nissim, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan. I enjoyed working with them all, and I thank them for everything they have taught me. iii Contents Acknowledgments . iii Contents . iv List of Figures . vi Abstract . vii 1 Introduction1 1.1 Differential Privacy . .2 1.2 The Sample Complexity of Private Learning . .3 1.3 Our Contributions . .5 1.4 Additional Contributions . 10 2 Related Literature 15 2.1 The Computational Price of Differential Privacy . 15 2.2 Interactive Query Release . 18 2.3 Answering Adaptively Chosen Statistical Queries . 19 2.4 Other Related Work . 21 3 Background and Preliminaries 22 3.1 Differential privacy . 22 3.2 Preliminaries from Learning Theory . 24 3.3 Generalization Bounds for Points and Thresholds . 29 3.4 Private Learning . 30 3.5 Basic Differentially Private Mechanisms . 31 3.6 Concentration Bounds . 33 4 The Generalization Properties of Differential Privacy 34 4.1 Main Results . -
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space Adam Smith Shuang Song Abhradeep Thakurta Boston University Google Research, Brain Team Google Research, Brain Team [email protected] [email protected] [email protected] Abstract We revisit the problem of counting the number of distinct elements F0(D) in a data stream D, over a domain [u]. We propose an ("; δ)-differentially private algorithm that approximates F0(D) within a factor of (1 ± γ), and with additive error of p O( ln(1/δ)="), using space O(ln(ln(u)/γ)/γ2). We improve on the prior work at least quadratically and up to exponentially, in terms of both space and additive p error. Our additive error guarantee is optimal up to a factor of O( ln(1/δ)), n ln(u) 1 o and the space bound is optimal up to a factor of O min ln γ ; γ2 . We assume the existence of an ideal uniform random hash function, and ignore the space required to store it. We later relax this requirement by assuming pseudo- random functions and appealing to a computational variant of differential privacy, SIM-CDP. Our algorithm is built on top of the celebrated Flajolet-Martin (FM) sketch. We show that FM-sketch is differentially private as is, as long as there are p ≈ ln(1/δ)=(εγ) distinct elements in the data set. Along the way, we prove a structural result showing that the maximum of k i.i.d. random variables is statisti- cally close (in the sense of "-differential privacy) to the maximum of (k + 1) i.i.d. -
Calibrating Noise to Sensitivity in Private Data Analysis
Calibrating Noise to Sensitivity in Private Data Analysis Cynthia Dwork1, Frank McSherry1, Kobbi Nissim2, and Adam Smith3? 1 Microsoft Research, Silicon Valley. {dwork,mcsherry}@microsoft.com 2 Ben-Gurion University. [email protected] 3 Weizmann Institute of Science. [email protected] Abstract. We continue a line of research initiated in [10, 11] on privacy- preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = P i g(xi), where xi denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard devi- ation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation re- sults showing the increased value of interactive sanitization mechanisms over non-interactive. -
Magic Adversaries Versus Individual Reduction: Science Wins Either Way ?
Magic Adversaries Versus Individual Reduction: Science Wins Either Way ? Yi Deng1;2 1 SKLOIS, Institute of Information Engineering, CAS, Beijing, P.R.China 2 State Key Laboratory of Cryptology, P. O. Box 5159, Beijing ,100878,China [email protected] Abstract. We prove that, assuming there exists an injective one-way function f, at least one of the following statements is true: – (Infinitely-often) Non-uniform public-key encryption and key agreement exist; – The Feige-Shamir protocol instantiated with f is distributional concurrent zero knowledge for a large class of distributions over any OR NP-relations with small distinguishability gap. The questions of whether we can achieve these goals are known to be subject to black-box lim- itations. Our win-win result also establishes an unexpected connection between the complexity of public-key encryption and the round-complexity of concurrent zero knowledge. As the main technical contribution, we introduce a dissection procedure for concurrent ad- versaries, which enables us to transform a magic concurrent adversary that breaks the distribu- tional concurrent zero knowledge of the Feige-Shamir protocol into non-black-box construc- tions of (infinitely-often) public-key encryption and key agreement. This dissection of complex algorithms gives insight into the fundamental gap between the known universal security reductions/simulations, in which a single reduction algorithm or simu- lator works for all adversaries, and the natural security definitions (that are sufficient for almost all cryptographic primitives/protocols), which switch the order of qualifiers and only require that for every adversary there exists an individual reduction or simulator. 1 Introduction The seminal work of Impagliazzo and Rudich [IR89] provides a methodology for studying the lim- itations of black-box reductions. -
6.5 X 11.5 Doublelines.P65
Cambridge University Press 978-0-521-87282-9 - Algorithmic Game Theory Edited by Noam Nisan, Tim Roughgarden, Eva Tardos and Vijay V. Vazirani Copyright Information More information Algorithmic Game Theory Edited by Noam Nisan Hebrew University of Jerusalem Tim Roughgarden Stanford University Eva´ Tardos Cornell University Vijay V. Vazirani Georgia Institute of Technology © Cambridge University Press www.cambridge.org Cambridge University Press 978-0-521-87282-9 - Algorithmic Game Theory Edited by Noam Nisan, Tim Roughgarden, Eva Tardos and Vijay V. Vazirani Copyright Information More information cambridge university press Cambridge, New York,Melbourne, Madrid, Cape Town, Singapore, Sao˜ Paulo, Delhi Cambridge University Press 32 Avenue of the Americas, New York, NY 10013-2473, USA www.cambridge.org Information on this title: www.cambridge.org/9780521872829 C Noam Nisan, Tim Roughgarden, Eva´ Tardos, Vijay V. Vazirani 2007 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2007 Printed in the United States of America A catalog record for this book is available from the British Library. Library of Congress Cataloging in Publication Data Algorithmic game theory / edited by Noam Nisan ...[et al.]; foreword by Christos Papadimitriou. p. cm. Includes index. ISBN-13: 978-0-521-87282-9 (hardback) ISBN-10: 0-521-87282-0 (hardback) 1. Game theory. 2. Algorithms. I. Nisan, Noam. II. Title. QA269.A43 2007 519.3–dc22 2007014231 ISBN 978-0-521-87282-9 hardback Cambridge University Press has no responsibility for the persistence or accuracy of URLS for external or third-party Internet Web sites referred to in this publication and does not guarantee that any content on such Web sites is, or will remain, accurate or appropriate.