A Key Distribution Scheme in Broadcast Encryption Using Polynomial Interpolation
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
ACM Honors Eminent Researchers for Technical Innovations That Have Improved How We Live and Work 2016 Recipients Made Contribu
Contact: Jim Ormond 212-626-0505 [email protected] ACM Honors Eminent Researchers for Technical Innovations That Have Improved How We Live and Work 2016 Recipients Made Contributions in Areas Including Big Data Analysis, Computer Vision, and Encryption NEW YORK, NY, May 3, 2017 – ACM, the Association for Computing Machinery (www.acm.org), today announced the recipients of four prestigious technical awards. These leaders were selected by their peers for making significant contributions that have had far-reaching impact on how we live and work. The awards reflect achievements in file sharing, broadcast encryption, information visualization and computer vision. The 2016 recipients will be formally honored at the ACM Awards Banquet on June 24 in San Francisco. The 2016 Award Recipients include: • Mahadev Satyanarayanan, Michael L. Kazar, Robert N. Sidebotham, David A. Nichols, Michael J. West, John H. Howard, Alfred Z. Spector and Sherri M. Nichols, recipients of the ACM Software System Award for developing the Andrew File System (AFS). AFS was the first distributed file system designed for tens of thousands of machines, and pioneered the use of scalable, secure and ubiquitous access to shared file data. To achieve the goal of providing a common shared file system used by large networks of people, AFS introduced novel approaches to caching, security, management and administration. AFS is still in use today as both an open source system and as the file system in commercial applications. It has also inspired several cloud-based storage applications. The 2016 Software System Award recipients designed and built the Andrew File System in the 1980s while working as a team at the Information Technology Center (ITC), a partnership between Carnegie Mellon University and IBM. -
Compact E-Cash and Simulatable Vrfs Revisited
Compact E-Cash and Simulatable VRFs Revisited Mira Belenkiy1, Melissa Chase2, Markulf Kohlweiss3, and Anna Lysyanskaya4 1 Microsoft, [email protected] 2 Microsoft Research, [email protected] 3 KU Leuven, ESAT-COSIC / IBBT, [email protected] 4 Brown University, [email protected] Abstract. Efficient non-interactive zero-knowledge proofs are a powerful tool for solving many cryptographic problems. We apply the recent Groth-Sahai (GS) proof system for pairing product equations (Eurocrypt 2008) to two related cryptographic problems: compact e-cash (Eurocrypt 2005) and simulatable verifiable random functions (CRYPTO 2007). We present the first efficient compact e-cash scheme that does not rely on a ran- dom oracle. To this end we construct efficient GS proofs for signature possession, pseudo randomness and set membership. The GS proofs for pseudorandom functions give rise to a much cleaner and substantially faster construction of simulatable verifiable random functions (sVRF) under a weaker number theoretic assumption. We obtain the first efficient fully simulatable sVRF with a polynomial sized output domain (in the security parameter). 1 Introduction Since their invention [BFM88] non-interactive zero-knowledge proofs played an important role in ob- taining feasibility results for many interesting cryptographic primitives [BG90,GO92,Sah99], such as the first chosen ciphertext secure public key encryption scheme [BFM88,RS92,DDN91]. The inefficiency of these constructions often motivated independent practical instantiations that were arguably conceptually less elegant, but much more efficient ([CS98] for chosen ciphertext security). We revisit two important cryptographic results of pairing-based cryptography, compact e-cash [CHL05] and simulatable verifiable random functions [CL07], that have very elegant constructions based on non-interactive zero-knowledge proof systems, but less elegant practical instantiations. -
Data Structures Meet Cryptography: 3SUM with Preprocessing
Data Structures Meet Cryptography: 3SUM with Preprocessing Alexander Golovnev Siyao Guo Thibaut Horel Harvard NYU Shanghai MIT [email protected] [email protected] [email protected] Sunoo Park Vinod Vaikuntanathan MIT & Harvard MIT [email protected] [email protected] Abstract This paper shows several connections between data structure problems and cryptography against preprocessing attacks. Our results span data structure upper bounds, cryptographic applications, and data structure lower bounds, as summarized next. First, we apply Fiat–Naor inversion, a technique with cryptographic origins, to obtain a data structure upper bound. In particular, our technique yields a suite of algorithms with space S and (online) time T for a preprocessing version of the N-input 3SUM problem where S3 T = O(N 6). This disproves a strong conjecture (Goldstein et al., WADS 2017) that there is no data· structure − − that solves this problem for S = N 2 δ and T = N 1 δ for any constant δ > 0. e Secondly, we show equivalence between lower bounds for a broad class of (static) data struc- ture problems and one-way functions in the random oracle model that resist a very strong form of preprocessing attack. Concretely, given a random function F :[N] [N] (accessed as an oracle) we show how to compile it into a function GF :[N 2] [N 2] which→ resists S-bit prepro- cessing attacks that run in query time T where ST = O(N 2−→ε) (assuming a corresponding data structure lower bound on 3SUM). In contrast, a classical result of Hellman tells us that F itself can be more easily inverted, say with N 2/3-bit preprocessing in N 2/3 time. -
Simpson's Rule
Simpson's rule In numerical analysis, Simpson's rule is a method for numerical integration, the numerical approximation of definite integrals. Specifically, it is the following approximation for equally spaced subdivisions (where is even): (General Form) , where and . Simpson's rule also corresponds to the three-point Newton-Cotes quadrature rule. Simpson's rule can be derived by In English, the method is credited to the mathematician Thomas Simpson (1710–1761) of Leicestershire, England. However, approximating the integrand f (x) (in Johannes Kepler used similar formulas over 100 years prior, and for this reason the method is sometimes called Kepler's blue) by the quadratic interpolant P rule , or Keplersche Fassregel (Kepler's barrel rule) in German. (x) (in red) . Contents Quadratic interpolation Averaging the midpoint and the trapezoidal rules Undetermined coefficients Error Composite Simpson's rule An animation showing how Simpson's 3/8 rule Simpson's rule approximation Composite Simpson's 3/8 rule improves with more strips. Alternative extended Simpson's rule Simpson's rules in the case of narrow peaks Composite Simpson's rule for irregularly spaced data See also Notes References External links Quadratic interpolation One derivation replaces the integrand by the quadratic polynomial (i.e. parabola) which takes the same values as at the end points a and b and the midpoint m = ( a + b) / 2. One can use Lagrange polynomial interpolation to find an expression for this polynomial, Using integration by substitution one can show that [1] Introducing the step size this is also commonly written as Because of the factor Simpson's rule is also referred to as Simpson's 1/3 rule (see below for generalization). -
Human Identity and the Quest to Replace Passwords Continued
Human identity and the quest to replace passwords continued Sirvan Almasi William J.Knottenbelt Department of Computing Department of Computing Imperial College London Imperial College London London, UK London, UK [email protected] [email protected] Abstract—Identification and authentication are vital Blockchain is proving to have many more important ap- components of many online services. For authentica- plications beyond its primary conceived application of tion, password has remained the most practical solution digital cash (Bitcoin [3] since 2009). Using it as a public- despite it being a weak form of security; whilst public- key cryptography is more secure, developing a practical key infrastructure (PKI) and identity management is two system has been an impediment to its adoption. For important examples that have tremendous implications for identification there is a lack of widely adopted protocol web applications. The likes of Namecoin [4], Blockstack [5] that isn’t dependent on trusted third parties or a and Certcoin [6] are examples of organisations attempting centralised public-key infrastructure. The Fiat–Shamir to recreate DNS, identity and certification services using identification scheme solved this issue but by assuming there was a single identity issuer to begin with, whilst blockchain. Other examples that have used the blockchain in reality you would have multiple identity issuers. as a PKI include [7] for IoT devices and [8] for identity Here we introduce deeID, a blockchain-based public- management. key infrastructure that enables multiple Fiat–Shamir The role and security of our identity in the digital identity issuers for identification. At the same time the ecosystem has taken a centre stage as the significance system is used for authentication that enables better than password security whilst remaining practical. -
Elementary Numerical Methods and Computing with Python
Elementary Numerical Methods and computing with Python Steven Pav1 Mark McClure2 April 14, 2016 1Portions Copyright c 2004-2006 Steven E. Pav. Permission is granted to copy, dis- tribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. 2Copyright c 2016 Mark McClure. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Ver- sion 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled ”GNU Free Documentation License”. 2 Contents Preface 7 1 Introduction 9 1.1 Examples ............................... 9 1.2 Iteration ................................ 11 1.3 Topics ................................. 12 2 Some mathematical preliminaries 15 2.1 Series ................................. 15 2.1.1 Geometric series ....................... 15 2.1.2 The integral test ....................... 17 2.1.3 Alternating Series ...................... 20 2.1.4 Taylor’s Theorem ....................... 21 Exercises .................................. 25 3 Computer arithmetic 27 3.1 Strange arithmetic .......................... 27 3.2 Error .................................. 28 3.3 Computer numbers .......................... 29 3.3.1 Types of numbers ...................... 29 3.3.2 Floating point numbers ................... 30 3.3.3 Distribution of computer numbers ............. 31 3.3.4 Exploring numbers with Python .............. 32 3.4 Loss of Significance .......................... 33 Exercises .................................. 36 4 Finding Roots 37 4.1 Bisection ............................... 38 4.1.1 Modifications ........................ -
Approximation of Finite Population Totals Using Lagrange Polynomial
Open Journal of Statistics, 2017, 7, 689-701 http://www.scirp.org/journal/ojs ISSN Online: 2161-7198 ISSN Print: 2161-718X Approximation of Finite Population Totals Using Lagrange Polynomial Lamin Kabareh1*, Thomas Mageto2, Benjamin Muema2 1Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi, Kenya 2Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya How to cite this paper: Kabareh, L., Mage- Abstract to, T. and Muema, B. (2017) Approximation of Finite Population Totals Using Lagrange Approximation of finite population totals in the presence of auxiliary infor- Polynomial. Open Journal of Statistics, 7, 689- mation is considered. A polynomial based on Lagrange polynomial is pro- 701. posed. Like the local polynomial regression, Horvitz Thompson and ratio es- https://doi.org/10.4236/ojs.2017.74048 timators, this approximation technique is based on annual population total in Received: July 9, 2017 order to fit in the best approximating polynomial within a given period of Accepted: August 15, 2017 time (years) in this study. This proposed technique has shown to be unbiased Published: August 18, 2017 under a linear polynomial. The use of real data indicated that the polynomial Copyright © 2017 by authors and is efficient and can approximate properly even when the data is unevenly Scientific Research Publishing Inc. spaced. This work is licensed under the Creative Commons Attribution International Keywords License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Lagrange Polynomial, Approximation, Finite Population Total, Open Access Auxiliary Information, Local Polynomial Regression, Horvitz Thompson and Ratio Estimator 1. Introduction This study is using an approximation technique to approximate the finite popu- lation total called the Lagrange polynomial that doesn’t require any selection of bandwidth as in the case of local polynomial regression estimator. -
Polynomial Interpolation 1
CREACOMP e−Schulung von Kreativität und Problemlösungskompetenz Prof. Dr. Bruno BUCHBERGER Prof. Dr. Erich Peter KLEMENT Prof. Dr. Günter PILZ und Mitarbeiter der Institute ALGEBRA, FLLL und RISC 0 CREACOMP Endbericht Inhalt CREACOMP ................................................................................................................1 Zusammenfassung ...........................................................................................1 Benutzung der CREACOMP Software .......................................................2 Kurzbeschreibung der Lerneinheiten ..........................................................3 Theorema ..................................................................................................3 Elementary Set Theory .............................................................................3 Relations ...................................................................................................4 Equivalence Relations ..............................................................................4 Factoring Integers .....................................................................................5 Polynomial Interpolation 1 .........................................................................5 Polynomial Interpolation 2 .........................................................................6 Real Sequences 1 .....................................................................................6 Real Sequences 2 .....................................................................................7 -
E-Cash Payment Protocols and Systems
E-CASH PAYMENT PROTOCOLS AND SYSTEMS * MRS. J. NIMALA, Assistant Professor, PG and Research Department of Commerce with CA, Hindusthan College of Arts and Science, Coimbatore. TN INDIA ** MRS. N. MENAGA, Assistant Professor, PG and Research Department of Commerce with CA, Hindusthan College of Arts and Science, Coimbatore. TN INDIA Abstract E-cash is a payment system intended and implemented for making purchases over open networks such as the Internet. Require of a payment system which provides the electronic transactions are emergent at the similar moment that the exploit of Internet is budding in our daily life. Present days electronic payment systems have a major dilemma, they cannot switch the security and the user’s secrecy and at the same time these systems are secure on the cost of their users ambiguity. This paper shows the payment protocols for digital cash and discusses how a digital cash system can be fashioned by presenting a few of the recent day’s digital cash systems in details. We also offer a comparison and determine them mutually to see which one of them fulfils the properties for digital cash and the mandatory security level. Keywords: E-cash; Payment Protocol; Double Spending; Blind Signature. Introduction DigiCash is a private company founded in 1989 by Dr David Chaum. It has created an Internet money product, now patented, and called ‘ecash’. DigiCash’s ecash has been used for many different types of transactions. It is a stored-value cryptographic coin system that facilitates Internet-based commerce using software that runs on personal computers. It provides a way to implement anonymous electronic payments in an environment of mutual mistrust between the bank and the system users. -
Language Program for Lagrange's Interpolation
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 5, Issue 6, November - December 2016 ISSN 2278-6856 ICT Tool: - ‘C’ Language Program for Lagrange’s Interpolation Pradip P. Kolhe1 , Dr Prakash R. Kolhe2 , Sanjay C. Gawande3 1Assistant Professor (Computer Sci.), ARIS Cell, Dr. PDKV, Akola (MS), India 2Associate Professor (Computer Sci.) (CAS), Dr. BSKKV, Dapoli (MS), India 3Assistant Professor (Mathematics), College of Agriculture, Dr. PDKV, Akola Abstract Although named after Joseph Louis Lagrange, who In mathematics to estimate an unknown data by analysing the published it in 1795, it was first discovered in 1779 by given referenced data Lagrange’s Interpolation technique is Edward Waring and it is also an easy consequence of a used. For the given data, (say ‘y’ at various ‘x’ in tabulated formula published in 1783 by Leonhard Euler. Lagrange form), the ‘y’ value corresponding to ‘x’ values can be found interpolation is susceptible to Runge's phenomenon, and by interpolation. the fact that changing the interpolation points requires Interpolation is the process of estimation of an unknown data recalculating the entire interpolant can make Newton by analysing the given reference data. In case of equally polynomials easier to use. Lagrange polynomials are used spaced ‘x’ values, a number of interpolation methods are in the Newton–Cotes method of numerical integration and available such as the Newton’s forward and backward in Shamir's secret sharing scheme in cryptography. interpolation, Gauss’s forward and backward interpolation, Bessel’s formula, Laplace-Everett’s formula etc. -
Federated Computing Research Conference, FCRC’96, Which Is David Wise, Steering Being Held May 20 - 28, 1996 at the Philadelphia Downtown Marriott
CRA Workshop on Academic Careers Federated for Women in Computing Science 23rd Annual ACM/IEEE International Symposium on Computing Computer Architecture FCRC ‘96 ACM International Conference on Research Supercomputing ACM SIGMETRICS International Conference Conference on Measurement and Modeling of Computer Systems 28th Annual ACM Symposium on Theory of Computing 11th Annual IEEE Conference on Computational Complexity 15th Annual ACM Symposium on Principles of Distributed Computing 12th Annual ACM Symposium on Computational Geometry First ACM Workshop on Applied Computational Geometry ACM/UMIACS Workshop on Parallel Algorithms ACM SIGPLAN ‘96 Conference on Programming Language Design and Implementation ACM Workshop of Functional Languages in Introductory Computing Philadelphia Skyline SIGPLAN International Conference on Functional Programming 10th ACM Workshop on Parallel and Distributed Simulation Invited Speakers Wm. A. Wulf ACM SIGMETRICS Symposium on Burton Smith Parallel and Distributed Tools Cynthia Dwork 4th Annual ACM/IEEE Workshop on Robin Milner I/O in Parallel and Distributed Systems Randy Katz SIAM Symposium on Networks and Information Management Sponsors ACM CRA IEEE NSF May 20-28, 1996 SIAM Philadelphia, PA FCRC WELCOME Organizing Committee Mary Jane Irwin, Chair Penn State University Steve Mahaney, Vice Chair Rutgers University Alan Berenbaum, Treasurer AT&T Bell Laboratories Frank Friedman, Exhibits Temple University Sampath Kannan, Student Coordinator Univ. of Pennsylvania Welcome to the second Federated Computing Research Conference, FCRC’96, which is David Wise, Steering being held May 20 - 28, 1996 at the Philadelphia downtown Marriott. This second Indiana University FCRC follows the same model of the highly successful first conference, FCRC’93, in Janice Cuny, Careers which nine constituent conferences participated. -
Web Search Via Hub Synthesis
Web Search Via Hub Synthesis Dimitris Achlioptas∗ Amos Fiaty Anna R. Karlinz Frank McSherryx Small have continual plodders ever won save base authority Page [6, 25, 18] developed a highly successful search en- from others books. gine, Google [17], which orders search results according to — William Shakespeare, Loves Labours Lost. Act i. Sc. 1. PageRank, a measure of authority of the underlying page. They define themselves in terms of what they oppose. Both approaches use, chiefly, text matching to determine — George Will, On conservatives, Newsweek 30 Sep 74. the set of candidate answers to a query, and then rely on linkage information to define a ranking of these candidate Abstract web pages. Unfortunately, as every occasionally frustrated web-searcher can attest, text matching can run into trou- We present a model for web search that captures in a uni- ble with two ubiquitous phenomena: synonymy (where two fied manner three critical components of the problem: how distinct terms, like “terrorist” and “freedom-fighter”, refer the link structure of the web is generated, how the content to the same thing) and polysemy (where a single word or of a web document is generated, and how a human searcher phrase, like “bug”, has multiple distinct meanings). Syn- generates a query. The key to this unification lies in cap- onymy may cause important pages to be overlooked, while turing the correlations between these components in terms polysemy may cause authoritative pages on topics other of proximity in a shared latent semantic space. Given such than the search topic to be included. a combined model, the correct answer to a search query is Synonymy and polysemy, as problems in information well defined, and thus it becomes possible to evaluate web retrieval, long precede the advent of the web and have re- search algorithms rigorously.