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Institution

IBM

CompanyArmonk, New York, United States
About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.


Papers
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Journal ArticleDOI
TL;DR: For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. as discussed by the authors introduces the basic concepts in the design and analysis of randomized algorithms and provides a comprehensive and representative selection of the algorithms that might be used in each of these areas.
Abstract: For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.

1,220 citations

Proceedings ArticleDOI
Roberto J. Bayardo1, Rakesh Agrawal1
05 Apr 2005
TL;DR: This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization, and presents a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develops data-management strategies to reduce reliance on expensive operations such as sorting.
Abstract: Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.

1,220 citations

Proceedings ArticleDOI
Moni Naor1, Moti Yung1
01 Apr 1990
TL;DR: This work shows how to construct a public-key cryptosystem (as originally defined by DiNe and Hellman) secure against chosen ciphertezt attacks, given aPublic-Key cryptosystern secure against passive eavesdropping and a noninteractive zero-knowledge proof system in the shared string model.
Abstract: We show how to construct a public-key cryptosystem (as originally defined by DiNe and Hellman) secure against chosen ciphertezt attacks, given a public-key cryptosystern secure against passive eavesdropping and a noninteractive zero-knowledge proof system in the shared string model. No such secure cryptosystems were known before. A concrete implementation can be based on quadratic residuosity intractability.

1,220 citations

Journal ArticleDOI
Frank Stern1
TL;DR: In this paper, the response of a two-dimensional electron gas to a longitudinal electric field of arbitrary wave vector and frequency is calculated in the selfconsistent field approximation, and the results are used to find the asymptotic screened Coulomb potential and the plasmon dispersion for a plane of electrons imbedded in a three-dimensional dielectric.
Abstract: The response of a two-dimensional electron gas to a longitudinal electric field of arbitrary wave vector and frequency is calculated in the self-consistent-field approximation. The results are used to find the asymptotic screened Coulomb potential and the plasmon dispersion for a plane of electrons imbedded in a three-dimensional dielectric.

1,218 citations

Journal ArticleDOI
Richard A. Toupin1

1,218 citations


Authors

Showing all 134658 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Anil K. Jain1831016192151
Hyun-Chul Kim1764076183227
Rodney S. Ruoff164666194902
Tobin J. Marks1591621111604
Jean M. J. Fréchet15472690295
Albert-László Barabási152438200119
György Buzsáki15044696433
Stanislas Dehaene14945686539
Philip S. Yu1481914107374
James M. Tour14385991364
Thomas P. Russell141101280055
Naomi J. Halas14043582040
Steven G. Louie13777788794
Daphne Koller13536771073
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202330
2022137
20213,163
20206,336
20196,427
20186,278