New Techniques for Cryptanalysis of Hash Functions and Improved Attacks on Snefru*
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11Th International Conference for Internet Technology and Secured Transactions
Analysis of PRNGs with Large State Spaces and Structural Improvements Gabriele Spenger, Jörg Keller Faculty of Mathematics and Computer Science FernUniversität in Hagen 58084 Hagen, Germany [email protected], [email protected] Abstract usually been subject for a lot of scientific work trying to find any kinds of weaknesses before their standardization. But there are also occasions where the The security of cryptographic functions such as standards do not provide the right tools for an pseudo random number generators (PRNGs) can application. One example is the application on low cost usually not be mathematically proven. Instead, RFID transmitters, which only allow a very low statistical properties of the generator are commonly complexity of the algorithms due to the cost restraints evaluated using standardized test batteries on a (the cost is mostly driven by the required chip area [1]). limited number of output values. This paper In these cases, it might be required to use a non- demonstrates that valuable additional information standardized algorithm. This implies the danger of about the properties of the algorithm can be gathered choosing an algorithm that does not provide the by analyzing the state space. As the state space for necessary security. practical use cases is usually huge, two approaches Besides the algorithms themselves also a couple of are presented to make this analysis manageable. methods for the assessment of the suitability of Results for a practical application of these cryptographic functions have been standardized. approaches to the algorithms AKARI and A5/1 are Examples for methods for assessing the security of provided, giving new insights about the suitability of PRNGs are the Marsaglia suite of Tests of these PRNGs for security applications. -
Using Map Service API for Driving Cycle Detection for Wearable GPS Data: Preprint
Using Map Service API for Driving Cycle Detection for Wearable GPS Data Preprint Lei Zhu and Jeffrey Gonder National Renewable Energy Laboratory To be presented at Transportation Research Board (TRB) 97th Annual Meeting Washington, DC January 7-11, 2018 NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Conference Paper NREL/CP-5400-70474 December 2017 Contract No. DE-AC36-08GO28308 NOTICE The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC (Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for US Government purposes. This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. -
Exact Learning of Sequences from Queries and Trackers
UNIVERSITY OF CALIFORNIA, IRVINE Exact Learning of Sequences from Queries and Trackers DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Pedro Ascens~ao Ferreira Matias Dissertation Committee: Distinguished Professor Michael T. Goodrich, Chair Distinguished Professor David Eppstein Professor Sandy Irani 2021 Chapter 2 c 2020 Springer Chapter 4 c 2021 Springer All other materials c 2021 Pedro Ascens~aoFerreira Matias DEDICATION To my parents Margarida and Lu´ıs,to my sister Ana and to my brother Luisito, for the encouragement in choosing my own path, and for always being there. { Senhorzinho Doutorzinho ii TABLE OF CONTENTS Page COVER DEDICATION ii TABLE OF CONTENTS iii LIST OF FIGURES v ACKNOWLEDGMENTS vii VITA ix ABSTRACT OF THE DISSERTATION xii 1 Introduction 1 1.1 Learning Strings . .3 1.2 Learning Paths in a Graph . .4 1.3 Literature Overview on Exact Learning and Other Learning Models . .5 2 Learning Strings 12 2.1 Introduction . 12 2.1.1 Related Work . 14 2.1.2 Our Results . 18 2.1.3 Preliminaries . 19 2.2 Substring Queries . 20 2.2.1 Uncorrupted Periodic Strings of Known Size . 22 2.2.2 Uncorrupted Periodic Strings of Unknown Size . 25 2.2.3 Corrupted Periodic Strings . 30 2.3 Subsequence Queries . 36 2.4 Jumbled-index Queries . 39 2.5 Conclusion and Open Questions . 46 3 Learning Paths in Planar Graphs 48 3.1 Introduction . 48 3.1.1 Related Work . 50 3.1.2 Definitions . 52 iii 3.2 Approximation algorithm . 53 3.2.1 Lower bound on OPT ......................... -
David Wong Snefru
SHA-3 vs the world David Wong Snefru MD4 Snefru MD4 Snefru MD4 MD5 Merkle–Damgård SHA-1 SHA-2 Snefru MD4 MD5 Merkle–Damgård SHA-1 SHA-2 Snefru MD4 MD5 Merkle–Damgård SHA-1 SHA-2 Snefru MD4 MD5 Merkle–Damgård SHA-1 SHA-2 Keccak BLAKE, Grøstl, JH, Skein Outline 1.SHA-3 2.derived functions 3.derived protocols f permutation-based cryptography AES is a permutation input AES output AES is a permutation 0 input 0 0 0 0 0 0 0 key 0 AES 0 0 0 0 0 0 output 0 Sponge Construction f Sponge Construction 0 0 0 1 0 0 0 1 f 0 1 0 0 0 0 0 1 Sponge Construction 0 0 0 1 r 0 0 0 1 f 0 1 0 0 c 0 0 0 1 Sponge Construction 0 0 0 1 r 0 0 0 1 f r c 0 1 0 0 0 0 0 0 c 0 0 0 0 0 1 0 0 key 0 AES 0 0 0 0 0 0 0 Sponge Construction message 0 1 0 1 0 ⊕ 1 0 0 f 0 0 0 0 0 1 0 0 Sponge Construction message 0 0 0 ⊕ ⊕ 0 f 0 0 0 0 Sponge Construction message 0 0 0 ⊕ ⊕ 0 f f 0 0 0 0 Sponge Construction message 0 0 0 ⊕ ⊕ ⊕ 0 f f 0 0 0 0 Sponge Construction message 0 0 0 ⊕ ⊕ ⊕ 0 f f f 0 0 0 0 Sponge Construction message 0 0 0 ⊕ ⊕ ⊕ 0 f f f 0 0 0 0 absorbing Sponge Construction message output 0 0 0 ⊕ ⊕ ⊕ 0 f f f 0 0 0 0 absorbing Sponge Construction message output 0 0 0 ⊕ ⊕ ⊕ 0 f f f f 0 0 0 0 absorbing Sponge Construction message output 0 0 0 ⊕ ⊕ ⊕ 0 f f f f 0 0 0 0 absorbing Sponge Construction message output 0 0 0 ⊕ ⊕ ⊕ 0 f f f f f 0 0 0 0 absorbing Sponge Construction message output 0 0 0 ⊕ ⊕ ⊕ 0 f f f f f 0 0 0 0 absorbing squeezing Keccak Guido Bertoni, Joan Daemen, Michaël Peeters and Gilles Van Assche 2007 SHA-3 competition 2012 2007 SHA-3 competition 2012 SHA-3 standard -
NISTIR 7620 Status Report on the First Round of the SHA-3
NISTIR 7620 Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition Andrew Regenscheid Ray Perlner Shu-jen Chang John Kelsey Mridul Nandi Souradyuti Paul NISTIR 7620 Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition Andrew Regenscheid Ray Perlner Shu-jen Chang John Kelsey Mridul Nandi Souradyuti Paul Information Technology Laboratory National Institute of Standards and Technology Gaithersburg, MD 20899-8930 September 2009 U.S. Department of Commerce Gary Locke, Secretary National Institute of Standards and Technology Patrick D. Gallagher, Deputy Director NISTIR 7620: Status Report on the First Round of the SHA-3 Cryptographic Hash Algorithm Competition Abstract The National Institute of Standards and Technology is in the process of selecting a new cryptographic hash algorithm through a public competition. The new hash algorithm will be referred to as “SHA-3” and will complement the SHA-2 hash algorithms currently specified in FIPS 180-3, Secure Hash Standard. In October, 2008, 64 candidate algorithms were submitted to NIST for consideration. Among these, 51 met the minimum acceptance criteria and were accepted as First-Round Candidates on Dec. 10, 2008, marking the beginning of the First Round of the SHA-3 cryptographic hash algorithm competition. This report describes the evaluation criteria and selection process, based on public feedback and internal review of the first-round candidates, and summarizes the 14 candidate algorithms announced on July 24, 2009 for moving forward to the second round of the competition. The 14 Second-Round Candidates are BLAKE, BLUE MIDNIGHT WISH, CubeHash, ECHO, Fugue, Grøstl, Hamsi, JH, Keccak, Luffa, Shabal, SHAvite-3, SIMD, and Skein. -
1 Abelian Group 266 Absolute Value 307 Addition Mod P 427 Additive
1 abelian group 266 certificate authority 280 absolute value 307 certificate of primality 405 addition mod P 427 characteristic equation 341, 347 additive identity 293, 497 characteristic of field 313 additive inverse 293 cheating xvi, 279, 280 adjoin root 425, 469 Chebycheff’s inequality 93 Advanced Encryption Standard Chebycheff’s theorem 193 (AES) 100, 106, 159 Chinese Remainder Theorem 214 affine cipher 13 chosen-plaintext attack xviii, 4, 14, algorithm xix, 150 141, 178 anagram 43, 98 cipher xvii Arithmetica key exchange 183 ciphertext xvii, 2 Artin group 185 ciphertext-only attack xviii, 4, 14, 142 ASCII xix classic block interleaver 56 asymmetric cipher xviii, 160 classical cipher xviii asynchronous cipher 99 code xvii Atlantic City algorithm 153 code-book attack 105 attack xviii common divisor 110 authentication 189, 288 common modulus attack 169 common multiple 110 baby-step giant-step 432 common words 32 Bell’s theorem 187 complex analysis 452 bijective 14, 486 complexity 149 binary search 489 composite function 487 binomial coefficient 19, 90, 200 compositeness test 264 birthday paradox 28, 389 composition of permutations 48 bit operation 149 compression permutation 102 block chaining 105 conditional probability 27 block cipher 98, 139 confusion 99, 101 block interleaver 56 congruence 130, 216 Blum integer 164 congruence class 130, 424 Blum–Blum–Shub generator 337 congruential generator 333 braid group 184 conjugacy problem 184 broadcast attack 170 contact method 44 brute force attack 3, 14 convolution product 237 bubble sort 490 coprime -
Data Intensive Dynamic Scheduling Model and Algorithm for Cloud Computing Security Md
1796 JOURNAL OF COMPUTERS, VOL. 9, NO. 8, AUGUST 2014 Data Intensive Dynamic Scheduling Model and Algorithm for Cloud Computing Security Md. Rafiqul Islam1,2 1Computer Science Department, American International University Bangladesh, Dhaka, Bangladesh 2Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh Email: [email protected] Mansura Habiba Computer Science Department, American International University Bangladesh, Dhaka, Bangladesh Email: [email protected] Abstract—As cloud is growing immensely, different point cloud computing has gained widespread acceptance. types of data are getting more and more dynamic in However, without appropriate security and privacy terms of security. Ensuring high level of security for solutions designed for clouds, cloud computing becomes all data in storages is highly expensive and time a huge failure [2, 3]. Amazon provides a centralized consuming. Unsecured services on data are also cloud computing consisting simple storage services (S3) becoming vulnerable for malicious threats and data and elastic compute cloud (EC2). Google App Engine is leakage. The main reason for this is that, the also an example of cloud computing. While these traditional scheduling algorithms for executing internet-based online services do provide huge amounts different services on data stored in cloud usually of storage space and customizable computing resources. sacrifice security privilege in order to achieve It is eliminating the responsibility of local machines for deadline. To provide adequate security without storing and maintenance of data. Considering various sacrificing cost and deadline for real time data- kinds of data for each user stored in the cloud and the intensive cloud system, security aware scheduling demand of long term continuous assurance of their data algorithm has become an effective and important safety, security is one of the prime concerns for the feature. -
Cuckoo Cycle: a Memory Bound Graph-Theoretic Proof-Of-Work
Cuckoo Cycle: a memory bound graph-theoretic proof-of-work John Tromp December 31, 2014 Abstract We introduce the first graph-theoretic proof-of-work system, based on finding small cycles or other structures in large random graphs. Such problems are trivially verifiable and arbitrarily scalable, presumably requiring memory linear in graph size to solve efficiently. Our cycle finding algorithm uses one bit per edge, and up to one bit per node. Runtime is linear in graph size and dominated by random access latency, ideal properties for a memory bound proof-of-work. We exhibit two alternative algorithms that allow for a memory-time trade-off (TMTO)|decreased memory usage, by a factor k, coupled with increased runtime, by a factor Ω(k). The constant implied in Ω() gives a notion of memory-hardness, which is shown to be dependent on cycle length, guiding the latter's choice. Our algorithms are shown to parallelize reasonably well. 1 Introduction A proof-of-work (PoW) system allows a verifier to check with negligible effort that a prover has expended a large amount of computational effort. Originally introduced as a spam fighting measure, where the effort is the price paid by an email sender for demanding the recipient's attention, they now form one of the cornerstones of crypto currencies. As proof-of-work for new blocks of transactions, Bitcoin [1] adopted Adam Back's hashcash [2]. Hashcash entails finding a nonce value such that application of a cryptographic hash function to this nonce and the rest of the block header, results in a number below a target threshold1. -
Performance Analysis of Cryptographic Hash Functions Suitable for Use in Blockchain
I. J. Computer Network and Information Security, 2021, 2, 1-15 Published Online April 2021 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijcnis.2021.02.01 Performance Analysis of Cryptographic Hash Functions Suitable for Use in Blockchain Alexandr Kuznetsov1 , Inna Oleshko2, Vladyslav Tymchenko3, Konstantin Lisitsky4, Mariia Rodinko5 and Andrii Kolhatin6 1,3,4,5,6 V. N. Karazin Kharkiv National University, Svobody sq., 4, Kharkiv, 61022, Ukraine E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] 2 Kharkiv National University of Radio Electronics, Nauky Ave. 14, Kharkiv, 61166, Ukraine E-mail: [email protected] Received: 30 June 2020; Accepted: 21 October 2020; Published: 08 April 2021 Abstract: A blockchain, or in other words a chain of transaction blocks, is a distributed database that maintains an ordered chain of blocks that reliably connect the information contained in them. Copies of chain blocks are usually stored on multiple computers and synchronized in accordance with the rules of building a chain of blocks, which provides secure and change-resistant storage of information. To build linked lists of blocks hashing is used. Hashing is a special cryptographic primitive that provides one-way, resistance to collisions and search for prototypes computation of hash value (hash or message digest). In this paper a comparative analysis of the performance of hashing algorithms that can be used in modern decentralized blockchain networks are conducted. Specifically, the hash performance on different desktop systems, the number of cycles per byte (Cycles/byte), the amount of hashed message per second (MB/s) and the hash rate (KHash/s) are investigated. -
Some Attacks on Merkle-Damgård Hashes
Overview Some Attacks on Merkle-Damg˚ardHashes John Kelsey, NIST and KU Leuven May 8, 2018 m m m m ||10*L 0 1 2 3 iv F h F h F h F h 0 1 2 final Introduction 1 / 63 Overview I Cryptographic Hash Functions I Thinking About Collisions I Merkle-Damg˚ardhashing I Joux Multicollisions[2004] I Long-Message Second Preimage Attacks[1999,2004] I Herding and the Nostradamus Attack[2005] Introduction 2 / 63 Why Talk About These Results? I These are very visual results{looking at the diagram often explains the idea. I The results are pretty accessible. I Help you think about what's going on inside hashing constructions. Introduction 3 / 63 Part I: Preliminaries/Review I Hash function basics I Thinking about collisions I Merkle-Damg˚ardhash functions Introduction 4 / 63 Cryptographic Hash Functions I Today, they're the workhorse of crypto. I Originally: Needed for digital signatures I You can't sign 100 MB message{need to sign something short. I \Message fingerprint" or \message digest" I Need a way to condense long message to short string. I We need a stand-in for the original message. I Take a long, variable-length message... I ...and map it to a short string (say, 128, 256, or 512 bits). Cryptographic Hash Functions 5 / 63 Properties What do we need from a hash function? I Collision resistance I Preimage resistance I Second preimage resistance I Many other properties may be important for other applications Note: cryptographic hash functions are designed to behave randomly. Cryptographic Hash Functions 6 / 63 Collision Resistance The core property we need. -
Graph Algorithms: the Core of Graph Analytics
Graph Algorithms: The Core of Graph Analytics Melli Annamalai and Ryota Yamanaka, Product Management, Oracle August 27, 2020 AskTOM Office Hours: Graph Database and Analytics • Welcome to our AskTOM Graph Office Hours series! We’re back with new product updates, use cases, demos and technical tips https://asktom.oracle.com/pls/apex/asktom.search?oh=3084 • Sessions will be held about once a month • Subscribe at the page above for updates on upcoming session topics & dates And submit feedback, questions, topic requests, and view past session recordings • Note: Spatial now has a new Office Hours series for location analysis & mapping features in Oracle Database: https://asktom.oracle.com/pls/apex/asktom.search?oh=7761 2 Copyright © 2020, Oracle and/or its affiliates Safe harbor statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation. 3 Copyright © 2020, Oracle and/or its affiliates Agenda 1. Introduction to Graph Algorithms Melli 2. Graph Algorithms Use Cases Melli 3. Running Graph Algorithms Ryota 4. Scalability in Graph Analytics Ryota Melli Ryota Nashua, New Hampshire, USA Bangkok, Thailand @AnnamalaiMelli @ryotaymnk -
Pollard's Rho Method
Computational Number Theory and Algebra June 20, 2012 Lecture 18 Lecturers: Markus Bl¨aser,Chandan Saha Scribe: Chandan Saha In the last class, we have discussed the Pollard-Strassen's integer factoring algorithm that has a run- ning time of O~(s(N)1=2) bit operations, where s(N) is the smallest prime factor of the input (composite) number N. This algorithm has a space complexity of O~(s(N)1=2) bits. In today's class, we will discuss a heuristic, randomized algorithm - namely, Pollard's rho method, whose `believed' expected time complexity is O~(s(N)1=2) bit operations, and which uses only O~(1) space! Although, a rigorous analysis of Pollard's rho method is still open, in practice it is observed that Pollard's rho method is more efficient than Pollard- Strassen factoring algorithm (partly because of the low memory usage of the former). Today, we will cover the following topics: • Pollard's rho method, and • Introduction to smooth numbers. 1 Pollard's rho method Let N be a number that is neither a prime nor a perfect power, and p the smallest prime factor of N. If we pick numbers x0; x1; x2;::: from ZN uniformly, independently at random then after at most p + 1 such pickings we have (for the first time) two numbers xi; xs with i < s such that xi = xs mod p. Since N is not a perfect power, there is another prime factor q > p of N. Since the numbers xi and xs are randomly chosen from ZN , by the Chinese remaindering theorem, xi 6= xs mod q with probability 1 − 1=q even under the condition that xi = xs mod p.