All-to-All Gradecast using Coding with Byzantine Failures
|
|
- Denis Glenn
- 5 years ago
- Views:
Transcription
1 All-to-All Gradecast using Coding with Byzantine Failures John Bridgman Vijay Garg Parallel and Distributed Systems Lab (PDSL) at The University of Texas at Austin Presented at SSS 2012 October 4, All-to-All Gradecast using Coding with Byzantine Failures 1/28
2 Motivating Scenario Want to compute some global function For example, the average of the values at each process No faults: everyone transmit and compute No problems All-to-All Gradecast using Coding with Byzantine Failures 2/28
3 With Faulty Processes n processes At most t faulty processes johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 3/28
4 Example 64 A 73 B 32 C 100 F 20 E XX D johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
5 Example 64 A B C F E D johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
6 Example A B C F 32 E 32 D 32 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
7 Example A 93 B 0 C 8 F 12 E 1 D johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
8 Example (64,73,32,0,20,100) (64,73,32,93,20,100) A B (64,73,32,8,20,100) C (64,73,32,12,20,100) F (64,73,32,1,20,100) E D johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
9 Example (64,73,32,0,20,100) (64,73,32,93,20,100) A B (64,73,32,8,20,100) C (64,73,32,12,20,100) F (64,73,32,1,20,100) E D johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
10 Example A B C (64,73,32,1,20,100) (64,73,32,1,20,100) (64,73,32,1,20,100) F (64,73,32,1,20,100) E D (64,73,32,1,20,100) johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
11 Example A B C F E D johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
12 Example (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (65,23,33, 5,23,110) (64,73,32, 1,20,100) (64,73,32,12,20,100) (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (35,33,31, 9,13, 10) (64,73,32, 1,20,100) (64,73,32,12,20,100) F A (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (43,83,23,10,13,11) (64,73,32, 1,20,100) (64,73,32,12,20,100) E B (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (60,30,39,10,53,60) (64,73,32, 1,20,100) (64,73,32,12,20,100) D C (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (61,53,39, 9,20,100) (64,73,32, 1,20,100) (64,73,32,12,20,100) johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
13 Example (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (65,23,33, 5,23,110) (64,73,32, 1,20,100) (64,73,32,12,20,100) (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (35,33,31, 9,13, 10) (64,73,32, 1,20,100) (64,73,32,12,20,100) F A (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (43,83,23,10,13,11) (64,73,32, 1,20,100) (64,73,32,12,20,100) E B (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (60,30,39,10,53,60) (64,73,32, 1,20,100) (64,73,32,12,20,100) D C (64,73,32,93,20,100) (64,73,32, 0,20,100) (64,73,32, 8,20,100) (61,53,39, 9,20,100) (64,73,32, 1,20,100) (64,73,32,12,20,100) johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 4/28
14 Complexity 2n 2 messages mn 2 + mn 3 message bits O(mn 3 ) Can we do better when t << n? n : number of nodes m : message size t : maximum faulty nodes johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 5/28
15 Error Correcting Codes Send message over some channel with probability p to change symbols 1-p 1 1 p Alice p Bob p Alice wants to send message M Pick a code based on p Encode M to get S Send S Bob decodes S to get M johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 6/28
16 Systematic Codes Called systematic if the first part of S is M Encoding gives: (M) (M, parity) There are many such codes Example: Reed-Solomon codes [Reed and Solomon 1960] johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 7/28
17 How is this useful here? How is this useful here? Second broadcast sends redundant information At most t of the values different between processes Faulty channel indistinguishable from faulty process johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 8/28
18 Method Alice Bob [241, 86, 35, 35] [241, 86, 35, 40] All-to-All Gradecast using Coding with Byzantine Failures 9/28
19 Method Alice Bob [241, 86, 35, 35] [39, 78] [241, 86, 35, 40] Encode Reed-Solomon code over Galois field size 256: (35x x x x 254 )%( x + x 2 ) = x johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 9/28
20 Method Alice [39, 78] Bob [241, 86, 35, 35] [241, 86, 35, 40] All-to-All Gradecast using Coding with Byzantine Failures 9/28
21 Method Alice Bob [241, 86, 35, 35] [241, 86, 35, 40] [241, 86, 35, 40][39, 78] All-to-All Gradecast using Coding with Byzantine Failures 9/28
22 Method Alice Bob [241, 86, 35, 35] [241, 86, 35, 40] [241, 86, 35, 40][39, 78] Decode [241, 86, 35, 35] All-to-All Gradecast using Coding with Byzantine Failures 9/28
23 Basic Method At most t differences between processes Encode to tolerate t corruptions Only send parity Reduces message size johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 10/28
24 Example XX 3 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
25 Example XX 3 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
26 Example [241, 86, 35, 35] [241, 86, 35, 35] 1 2 [241, 86, 35, 40] 3 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
27 Example [241, 86, 35, 35] [39, 78] [241, 86, 35, 35] [39, 78] 1 2 [241, 86, 35, 40] [82, 30] 3 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
28 Example [39, 78] [39, 78] 1 2 [82, 30] 3 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
29 Example [241, 86, 35, 35] [39, 78] [39, 78] [82, 30] [22, 77] 1 2 [241, 86, 35, 35] [39, 78] [39, 78] [82, 30] [0, 136] [241, 86, 35, 40] [39, 78] [39, 78] [82, 30] 3 [121, 159] 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
30 Example [241, 86, 35, 35] [241, 86, 35, 35] [241, 86, 35, 40] [241, 49, 35, 35] 1 2 [241, 86, 35, 35] [241, 86, 35, 35] [241, 86, 35, 40] [241, 86, 129, 35] [241, 86, 35, 35] [241, 86, 35, 35] [241, 86, 35, 40] [157, 86, 35, 40] 3 4 johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 11/28
31 Method Application: Gradecast Gradecast is a broadcast algorithm that can tolerate Byzantine faults [Feldman, Micali 1988] Requires t < n/3 When P i gradecasts a value, every process P j receives v j [i] and a confidence level confidence j [i] {0, 1, 2} johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 12/28
32 Gradecast Properties Confidence is greater than zero implies same value P i, P j non-faulty and P k : confidence j [k] > 0 and confidence i [k] > 0 = value j [k] = value i [k] Process v i confidence i value i P 1 20 (2, 2, 2, 1) (20, 38, 10, 5) P 2 38 (2, 2, 2, 1) (20, 38, 10, 5) P 3 10 (2, 2, 2, 0) (20, 38, 10, ) P 4 XX XX XX johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 13/28
33 Gradecast Properties Confidence differs by at most one P i, P j non-faulty and P k : confidence i [k] confidence j [k] 1 Process v i confidence i value i P 1 20 (2, 2, 2, 1) (20, 38, 10, 5) P 2 38 (2, 2, 2, 1) (20, 38, 10, 5) P 3 10 (2, 2, 2, 0) (20, 38, 10, ) P 4 XX XX XX johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 14/28
34 Gradecast Properties Non-faulty source gets maximum confidence If P k is non-faulty, P i non-faulty: confidence i [k] = 2 and value i [k] = P k s initial value Process v i confidence i value i P 1 20 (2, 2, 2, 1) (20, 38, 10, 5) P 2 38 (2, 2, 2, 1) (20, 38, 10, 5) P 3 10 (2, 2, 2, 0) (20, 38, 10, ) P 4 XX XX XX johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 15/28
35 Algorithm Setup P i :: Inputs: v i : Input value for P i Common knowledge: n : The number of processes t : Maximum number of faulty processes Variables: V i [1..n] : Vector received in Step 2, initially Vecc i [1..2t + 1] : error correction vector for V i X i [1..n][1..n] : Matrix of decoded values in Step 3 Y i [1..n] : Vector of values computed in Step 3 Yecc i [1..2t + 1] : Error correction vector for Y i Z i [1..n][1..n] : Matrix of decoded values in Step 4 value i [1..n] : Vector of output values confidence i [1..n] : Vector of confidence levels johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 16/28
36 Algorithm Step One Though Three // Step 1: Initial Broadcast for j : 1 to n do P i.send(p j, v i ); end // Step 2: Receive, Encode, Broadcast encoding for j : 1 to n do V i [j] = P i.receive(p j ); end Vecc i = encode(v i ); for j : 1 to n do P i.send(p j, Vecc i ); end // Step 3: Receive encoding and process, encode result // and broadcast encoding for j : 1 to n do X i [j] = decode(v i, P i.receive(p j )); end j let Y i [j] = x if x s.t. {k : X i [k][j] = x} n t otherwise Y i [j] = Yecc i = encode(y i ); for j : 1 to n do P i.send(p j, Yecc i ); end johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 17/28
37 Algorithm Step Four // Step 4: Receive encoding, decode and compute final value for j : 1 to n do Z i [j] = decode(y i, P i.receive(p j )); end for j : 1 to n do if max x {k : Z i [k][j] = x} 2t + 1 then value i [j] = arg max x {k : Z i [k][j] = x} ; confidence i [j] = 2; elseif max x {k : Z i [k][j] = x} > t then value i [j] = arg max x {k : Z i [k][j] = x} ; confidence i [j] = 1; else value i [j] = ; confidence i [j] = 0; end end Output value i and confidence i. johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 18/28
38 Property (1) Theorem All non-faulty processes with positive confidence about process k have identical value[k]. Formally, P i, P j G, k : confidence i [k] > 0 confidence j [k] > 0 implies value i [k] = value j [k]. johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 19/28
39 Property (2) Theorem For any two non-faulty processes, the difference in their confidence levels for any process P k can differ by at most 1. Formally, P i, P j G, k : confidence i [k] confidence j [k] 1. johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 20/28
40 Property (3) Theorem If P k is non-faulty, then, all non-faulty processes P i have the value sent by process P k and their confidence level on this value is 2. Formally, P i, P k G : (confidence i [k] = 2) (value i [k] = v k ). johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 21/28
41 Gradecast Application: Byzantine Agreement Simple Byzantine Agreement algorithm by [Ben-Or, Dolev, Hoch 2010] that uses gradecast with O(mn 3 ) message bit complexity Can use our version of gradecast to get O(mtn 2 ) message bit complexity n : number of nodes m : message size t : maximum faulty nodes johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 22/28
42 Gradecast Application: Approximate Agreement [Ben-Or, Dolev, Hoch 2010] also give an approximate agreement algorithm using gradecast Can use our modified algorithm O(mkn 3 ) to O(mktn 2 ) reduction in message bit complexity n : number of nodes m : message size t : maximum faulty nodes k : rounds in approximate agreement johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 23/28
43 Related Work [Liang and Vaidya 2011] O(mn) bits for broadcast if m = Ω(n 6 ) otherwise O(nm + n 4 m 1/2 + n 6 ) [Friedman, Mostéfaoui, Rajsbaum and Raynal 2007] A mapping from a distributed agreement problem to a coding problem johnfbiii@utexas.edu, garg@ece.utexas.edu All-to-All Gradecast using Coding with Byzantine Failures 24/28
44 What about crash faults? Processes can only crash Translates to erasures Needs one half the parity length All-to-All Gradecast using Coding with Byzantine Failures 25/28
45 Conclusions Used Forward Error Correcting Codes to reduce message bit complexity of fault tolerant broadcast All-to-All Gradecast using Coding with Byzantine Failures 26/28
46 Future Work Apply to other distributed algorithms like simulated authentication [Srikanth and Toueg 1987] and interactive consistency [Hélary, Hurfin, Mostéfaoui, Raynal, and Tronel 2000] Derive lower bounds using coding theory results All-to-All Gradecast using Coding with Byzantine Failures 27/28
47 Thank you All-to-All Gradecast using Coding with Byzantine Failures 28/28
Deterministic Consensus Algorithm with Linear Per-Bit Complexity
Deterministic Consensus Algorithm with Linear Per-Bit Complexity Guanfeng Liang and Nitin Vaidya Department of Electrical and Computer Engineering, and Coordinated Science Laboratory University of Illinois
More informationThe Weighted Byzantine Agreement Problem
The Weighted Byzantine Agreement Problem Vijay K. Garg and John Bridgman Department of Electrical and Computer Engineering The University of Texas at Austin Austin, TX 78712-1084, USA garg@ece.utexas.edu,
More informationByzantine Agreement. Gábor Mészáros. Tatracrypt 2012, July 2 4 Smolenice, Slovakia. CEU Budapest, Hungary
CEU Budapest, Hungary Tatracrypt 2012, July 2 4 Smolenice, Slovakia Byzantium, 1453 AD. The Final Strike... Communication via Messengers The Byzantine Generals Problem Communication System Model Goal G
More informationError Detection and Correction: Hamming Code; Reed-Muller Code
Error Detection and Correction: Hamming Code; Reed-Muller Code Greg Plaxton Theory in Programming Practice, Spring 2005 Department of Computer Science University of Texas at Austin Hamming Code: Motivation
More informationTowards optimal synchronous counting
Towards optimal synchronous counting Christoph Lenzen Joel Rybicki Jukka Suomela MPI for Informatics MPI for Informatics Aalto University Aalto University PODC 5 July 3 Focus on fault-tolerance Fault-tolerant
More informationAgreement Protocols. CS60002: Distributed Systems. Pallab Dasgupta Dept. of Computer Sc. & Engg., Indian Institute of Technology Kharagpur
Agreement Protocols CS60002: Distributed Systems Pallab Dasgupta Dept. of Computer Sc. & Engg., Indian Institute of Technology Kharagpur Classification of Faults Based on components that failed Program
More informationCS505: Distributed Systems
Cristina Nita-Rotaru CS505: Distributed Systems. Required reading for this topic } Michael J. Fischer, Nancy A. Lynch, and Michael S. Paterson for "Impossibility of Distributed with One Faulty Process,
More informationByzantine Agreement. Gábor Mészáros. CEU Budapest, Hungary
CEU Budapest, Hungary 1453 AD, Byzantium Distibuted Systems Communication System Model Distibuted Systems Communication System Model G = (V, E) simple graph Distibuted Systems Communication System Model
More informationByzantine Vector Consensus in Complete Graphs
Byzantine Vector Consensus in Complete Graphs Nitin H. Vaidya University of Illinois at Urbana-Champaign nhv@illinois.edu Phone: +1 217-265-5414 Vijay K. Garg University of Texas at Austin garg@ece.utexas.edu
More informationOptimal and Player-Replaceable Consensus with an Honest Majority Silvio Micali and Vinod Vaikuntanathan
Computer Science and Artificial Intelligence Laboratory Technical Report MIT-CSAIL-TR-2017-004 March 31, 2017 Optimal and Player-Replaceable Consensus with an Honest Majority Silvio Micali and Vinod Vaikuntanathan
More informationLinear Algebra. F n = {all vectors of dimension n over field F} Linear algebra is about vectors. Concretely, vectors look like this:
15-251: Great Theoretical Ideas in Computer Science Lecture 23 Linear Algebra Linear algebra is about vectors. Concretely, vectors look like this: They are arrays of numbers. fig. by Peter Dodds # of numbers,
More informationQuantum Teleportation Pt. 1
Quantum Teleportation Pt. 1 PHYS 500 - Southern Illinois University April 17, 2018 PHYS 500 - Southern Illinois University Quantum Teleportation Pt. 1 April 17, 2018 1 / 13 Types of Communication In the
More informationLower Bounds for Achieving Synchronous Early Stopping Consensus with Orderly Crash Failures
Lower Bounds for Achieving Synchronous Early Stopping Consensus with Orderly Crash Failures Xianbing Wang 1, Yong-Meng Teo 1,2, and Jiannong Cao 3 1 Singapore-MIT Alliance, 2 Department of Computer Science,
More informationDiscrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Discussion 6A Solution
CS 70 Discrete Mathematics and Probability Theory Spring 2016 Rao and Walrand Discussion 6A Solution 1. Polynomial intersections Find (and prove) an upper-bound on the number of times two distinct degree
More informationLecture 3,4: Multiparty Computation
CS 276 Cryptography January 26/28, 2016 Lecture 3,4: Multiparty Computation Instructor: Sanjam Garg Scribe: Joseph Hui 1 Constant-Round Multiparty Computation Last time we considered the GMW protocol,
More informationGreat Theoretical Ideas in Computer Science
15-251 Great Theoretical Ideas in Computer Science Polynomials, Lagrange, and Error-correction Lecture 23 (November 10, 2009) P(X) = X 3 X 2 + + X 1 + Definition: Recall: Fields A field F is a set together
More informationOn Expected Constant-Round Protocols for Byzantine Agreement
On Expected Constant-Round Protocols for Byzantine Agreement Jonathan Katz Chiu-Yuen Koo Abstract In a seminal paper, Feldman and Micali show an n-party Byzantine agreement protocol in the plain model
More informationRound Complexity of Authenticated Broadcast with a Dishonest Majority
Round Complexity of Authenticated Broadcast with a Dishonest Majority Juan A. Garay Jonathan Katz Chiu-Yuen Koo Rafail Ostrovsky Abstract Broadcast among n parties in the presence of t n/3 malicious parties
More information9 THEORY OF CODES. 9.0 Introduction. 9.1 Noise
9 THEORY OF CODES Chapter 9 Theory of Codes After studying this chapter you should understand what is meant by noise, error detection and correction; be able to find and use the Hamming distance for a
More informationDegradable Agreement in the Presence of. Byzantine Faults. Nitin H. Vaidya. Technical Report #
Degradable Agreement in the Presence of Byzantine Faults Nitin H. Vaidya Technical Report # 92-020 Abstract Consider a system consisting of a sender that wants to send a value to certain receivers. Byzantine
More informationchannel of communication noise Each codeword has length 2, and all digits are either 0 or 1. Such codes are called Binary Codes.
5 Binary Codes You have already seen how check digits for bar codes (in Unit 3) and ISBN numbers (Unit 4) are used to detect errors. Here you will look at codes relevant for data transmission, for example,
More informationEarly-Deciding Consensus is Expensive
Early-Deciding Consensus is Expensive ABSTRACT Danny Dolev Hebrew University of Jerusalem Edmond Safra Campus 9904 Jerusalem, Israel dolev@cs.huji.ac.il In consensus, the n nodes of a distributed system
More informationSimple Bivalency Proofs of the Lower Bounds in Synchronous Consensus Problems
Simple Bivalency Proofs of the Lower Bounds in Synchronous Consensus Problems Xianbing Wang, Yong-Meng Teo, and Jiannong Cao Singapore-MIT Alliance E4-04-10, 4 Engineering Drive 3, Singapore 117576 Abstract
More informationCyclic Redundancy Check Codes
Cyclic Redundancy Check Codes Lectures No. 17 and 18 Dr. Aoife Moloney School of Electronics and Communications Dublin Institute of Technology Overview These lectures will look at the following: Cyclic
More informationEarly stopping: the idea. TRB for benign failures. Early Stopping: The Protocol. Termination
TRB for benign failures Early stopping: the idea Sender in round : :! send m to all Process p in round! k, # k # f+!! :! if delivered m in round k- and p " sender then 2:!! send m to all 3:!! halt 4:!
More informationCorrecting Localized Deletions Using Guess & Check Codes
55th Annual Allerton Conference on Communication, Control, and Computing Correcting Localized Deletions Using Guess & Check Codes Salim El Rouayheb Rutgers University Joint work with Serge Kas Hanna and
More informationEfficiently decodable codes for the binary deletion channel
Efficiently decodable codes for the binary deletion channel Venkatesan Guruswami (venkatg@cs.cmu.edu) Ray Li * (rayyli@stanford.edu) Carnegie Mellon University August 18, 2017 V. Guruswami and R. Li (CMU)
More informationModel Checking of Fault-Tolerant Distributed Algorithms
Model Checking of Fault-Tolerant Distributed Algorithms Part I: Fault-Tolerant Distributed Algorithms Annu Gmeiner Igor Konnov Ulrich Schmid Helmut Veith Josef Widder LOVE 2016 @ TU Wien Josef Widder (TU
More informationAGREEMENT PROBLEMS (1) Agreement problems arise in many practical applications:
AGREEMENT PROBLEMS (1) AGREEMENT PROBLEMS Agreement problems arise in many practical applications: agreement on whether to commit or abort the results of a distributed atomic action (e.g. database transaction)
More informationImplementing Uniform Reliable Broadcast with Binary Consensus in Systems with Fair-Lossy Links
Implementing Uniform Reliable Broadcast with Binary Consensus in Systems with Fair-Lossy Links Jialin Zhang Tsinghua University zhanggl02@mails.tsinghua.edu.cn Wei Chen Microsoft Research Asia weic@microsoft.com
More informationReliable Broadcast for Broadcast Busses
Reliable Broadcast for Broadcast Busses Ozalp Babaoglu and Rogerio Drummond. Streets of Byzantium: Network Architectures for Reliable Broadcast. IEEE Transactions on Software Engineering SE- 11(6):546-554,
More informationFinally the Weakest Failure Detector for Non-Blocking Atomic Commit
Finally the Weakest Failure Detector for Non-Blocking Atomic Commit Rachid Guerraoui Petr Kouznetsov Distributed Programming Laboratory EPFL Abstract Recent papers [7, 9] define the weakest failure detector
More informationInformation-Theoretic Lower Bounds on the Storage Cost of Shared Memory Emulation
Information-Theoretic Lower Bounds on the Storage Cost of Shared Memory Emulation Viveck R. Cadambe EE Department, Pennsylvania State University, University Park, PA, USA viveck@engr.psu.edu Nancy Lynch
More informationChapter 6 Reed-Solomon Codes. 6.1 Finite Field Algebra 6.2 Reed-Solomon Codes 6.3 Syndrome Based Decoding 6.4 Curve-Fitting Based Decoding
Chapter 6 Reed-Solomon Codes 6. Finite Field Algebra 6. Reed-Solomon Codes 6.3 Syndrome Based Decoding 6.4 Curve-Fitting Based Decoding 6. Finite Field Algebra Nonbinary codes: message and codeword symbols
More information6.895 PCP and Hardness of Approximation MIT, Fall Lecture 3: Coding Theory
6895 PCP and Hardness of Approximation MIT, Fall 2010 Lecture 3: Coding Theory Lecturer: Dana Moshkovitz Scribe: Michael Forbes and Dana Moshkovitz 1 Motivation In the course we will make heavy use of
More informationDistributed Systems Byzantine Agreement
Distributed Systems Byzantine Agreement He Sun School of Informatics University of Edinburgh Outline Finish EIG algorithm for Byzantine agreement. Number-of-processors lower bound for Byzantine agreement.
More informationFault-Tolerant Consensus
Fault-Tolerant Consensus CS556 - Panagiota Fatourou 1 Assumptions Consensus Denote by f the maximum number of processes that may fail. We call the system f-resilient Description of the Problem Each process
More informationByzantine Vector Consensus in Complete Graphs
Byzantine Vector Consensus in Complete Graphs Nitin H. Vaidya Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, Illinois, U.S.A. nhv@illinois.edu Vijay
More informationTime. Lakshmi Ganesh. (slides borrowed from Maya Haridasan, Michael George)
Time Lakshmi Ganesh (slides borrowed from Maya Haridasan, Michael George) The Problem Given a collection of processes that can... only communicate with significant latency only measure time intervals approximately
More informationBroadcast Amplification
Broadcast Amplification Martin Hirt, Ueli Maurer, Pavel Raykov {hirt,maurer,raykovp}@inf.ethz.ch ETH Zurich, Switzerland Abstract. A d-broadcast primitive is a communication primitive that allows a sender
More informationNetwork Algorithms and Complexity (NTUA-MPLA) Reliable Broadcast. Aris Pagourtzis, Giorgos Panagiotakos, Dimitris Sakavalas
Network Algorithms and Complexity (NTUA-MPLA) Reliable Broadcast Aris Pagourtzis, Giorgos Panagiotakos, Dimitris Sakavalas Slides are partially based on the joint work of Christos Litsas, Aris Pagourtzis,
More informationByzantine behavior also includes collusion, i.e., all byzantine nodes are being controlled by the same adversary.
Chapter 17 Byzantine Agreement In order to make flying safer, researchers studied possible failures of various sensors and machines used in airplanes. While trying to model the failures, they were confronted
More informationByzantine Agreement. Chapter Validity 190 CHAPTER 17. BYZANTINE AGREEMENT
190 CHAPTER 17. BYZANTINE AGREEMENT 17.1 Validity Definition 17.3 (Any-Input Validity). The decision value must be the input value of any node. Chapter 17 Byzantine Agreement In order to make flying safer,
More informationOn Expected Constant-Round Protocols for Byzantine Agreement
On Expected Constant-Round Protocols for Byzantine Agreement Jonathan Katz Chiu-Yuen Koo Abstract In a seminal paper, Feldman and Micali (STOC 88) show an n-party Byzantine agreement protocol tolerating
More information2. Polynomials. 19 points. 3/3/3/3/3/4 Clearly indicate your correctly formatted answer: this is what is to be graded. No need to justify!
1. Short Modular Arithmetic/RSA. 16 points: 3/3/3/3/4 For each question, please answer in the correct format. When an expression is asked for, it may simply be a number, or an expression involving variables
More informationLecture 6: Expander Codes
CS369E: Expanders May 2 & 9, 2005 Lecturer: Prahladh Harsha Lecture 6: Expander Codes Scribe: Hovav Shacham In today s lecture, we will discuss the application of expander graphs to error-correcting codes.
More informationAn Introduction to (Network) Coding Theory
An to (Network) Anna-Lena Horlemann-Trautmann University of St. Gallen, Switzerland April 24th, 2018 Outline 1 Reed-Solomon Codes 2 Network Gabidulin Codes 3 Summary and Outlook A little bit of history
More informationSignature-Free Broadcast-Based Intrusion Tolerance: Never Decide a Byzantine Value
Signature-Free Broadcast-Based Intrusion Tolerance: Never Decide a Byzantine Value Achour Mostefaoui, Michel Raynal To cite this version: Achour Mostefaoui, Michel Raynal. Signature-Free Broadcast-Based
More information5. Density evolution. Density evolution 5-1
5. Density evolution Density evolution 5-1 Probabilistic analysis of message passing algorithms variable nodes factor nodes x1 a x i x2 a(x i ; x j ; x k ) x3 b x4 consider factor graph model G = (V ;
More informationOptimal Resilience Asynchronous Approximate Agreement
Optimal Resilience Asynchronous Approximate Agreement Ittai Abraham, Yonatan Amit, and Danny Dolev School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel {ittaia, mitmit,
More informationLecture 3: Error Correcting Codes
CS 880: Pseudorandomness and Derandomization 1/30/2013 Lecture 3: Error Correcting Codes Instructors: Holger Dell and Dieter van Melkebeek Scribe: Xi Wu In this lecture we review some background on error
More informationOn the Number of Synchronous Rounds Required for Byzantine Agreement
On the Number of Synchronous Rounds Required for Byzantine Agreement Matthias Fitzi 1 and Jesper Buus Nielsen 2 1 ETH Zürich 2 University pf Aarhus Abstract. Byzantine agreement is typically considered
More informationI R I S A P U B L I C A T I O N I N T E R N E THE NOTION OF VETO NUMBER FOR DISTRIBUTED AGREEMENT PROBLEMS
I R I P U B L I C A T I O N I N T E R N E N o 1599 S INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTÈMES ALÉATOIRES A THE NOTION OF VETO NUMBER FOR DISTRIBUTED AGREEMENT PROBLEMS ROY FRIEDMAN, ACHOUR MOSTEFAOUI,
More informationCSCI-B609: A Theorist s Toolkit, Fall 2016 Oct 4. Theorem 1. A non-zero, univariate polynomial with degree d has at most d roots.
CSCI-B609: A Theorist s Toolkit, Fall 2016 Oct 4 Lecture 14: Schwartz-Zippel Lemma and Intro to Coding Theory Lecturer: Yuan Zhou Scribe: Haoyu Zhang 1 Roots of polynomials Theorem 1. A non-zero, univariate
More informationError Detection & Correction
Error Detection & Correction Error detection & correction noisy channels techniques in networking error detection error detection capability retransmition error correction reconstruction checksums redundancy
More informationAuthenticated Broadcast with a Partially Compromised Public-Key Infrastructure
Authenticated Broadcast with a Partially Compromised Public-Key Infrastructure S. Dov Gordon Jonathan Katz Ranjit Kumaresan Arkady Yerukhimovich Abstract Given a public-key infrastructure (PKI) and digital
More informationDiscrete Mathematics and Probability Theory Fall 2013 Vazirani Note 6
CS 70 Discrete Mathematics and Probability Theory Fall 2013 Vazirani Note 6 Error Correcting Codes We will consider two situations in which we wish to transmit information on an unreliable channel. The
More informationAn Introduction to (Network) Coding Theory
An Introduction to (Network) Coding Theory Anna-Lena Horlemann-Trautmann University of St. Gallen, Switzerland July 12th, 2018 1 Coding Theory Introduction Reed-Solomon codes 2 Introduction Coherent network
More informationBee s Strategy Against Byzantines Replacing Byzantine Participants
Bee s Strategy Against Byzantines Replacing Byzantine Participants by Roberto Baldoni, Silvia Banomi, Shlomi Dolev, Michel Raynal, Amitay Shaer Technical Report #18-05 September 21, 2018 The Lynne and
More informationGuess & Check Codes for Deletions, Insertions, and Synchronization
Guess & Check Codes for Deletions, Insertions, and Synchronization Serge Kas Hanna, Salim El Rouayheb ECE Department, Rutgers University sergekhanna@rutgersedu, salimelrouayheb@rutgersedu arxiv:759569v3
More informationAn Introduction to Low Density Parity Check (LDPC) Codes
An Introduction to Low Density Parity Check (LDPC) Codes Jian Sun jian@csee.wvu.edu Wireless Communication Research Laboratory Lane Dept. of Comp. Sci. and Elec. Engr. West Virginia University June 3,
More informationSelf-stabilizing Byzantine Agreement
Self-stabilizing Byzantine Agreement Ariel Daliot School of Engineering and Computer Science The Hebrew University, Jerusalem, Israel adaliot@cs.huji.ac.il Danny Dolev School of Engineering and Computer
More informationBenny Pinkas. Winter School on Secure Computation and Efficiency Bar-Ilan University, Israel 30/1/2011-1/2/2011
Winter School on Bar-Ilan University, Israel 30/1/2011-1/2/2011 Bar-Ilan University Benny Pinkas Bar-Ilan University 1 What is N? Bar-Ilan University 2 Completeness theorems for non-cryptographic fault-tolerant
More informationRandomized Protocols for Asynchronous Consensus
Randomized Protocols for Asynchronous Consensus Alessandro Panconesi DSI - La Sapienza via Salaria 113, piano III 00198 Roma, Italy One of the central problems in the Theory of (feasible) Computation is
More informationReplication predicates for dependent-failure algorithms
Replication predicates for dependent-failure algorithms Flavio Junqueira and Keith Marzullo Department of Computer Science and Engineering University of California, San Diego La Jolla, CA USA {flavio,
More informationIntroduction to Low-Density Parity Check Codes. Brian Kurkoski
Introduction to Low-Density Parity Check Codes Brian Kurkoski kurkoski@ice.uec.ac.jp Outline: Low Density Parity Check Codes Review block codes History Low Density Parity Check Codes Gallager s LDPC code
More informationExchanging Third-Party Information in a Network
David J. Love 1 Bertrand M. Hochwald Kamesh Krishnamurthy 1 1 School of Electrical and Computer Engineering Purdue University Beceem Communications ITA Workshop, 007 Information Exchange Some applications
More informationSignature-Free Asynchronous Byzantine Consensus with t < n/3 and O(n 2 ) Messages
Signature-Free Asynchronous Byzantine Consensus with t < n/3 and O(n 2 ) Messages Achour Mostefaoui, Moumen Hamouna, Michel Raynal To cite this version: Achour Mostefaoui, Moumen Hamouna, Michel Raynal.
More informationDistributed Source Coding Using LDPC Codes
Distributed Source Coding Using LDPC Codes Telecommunications Laboratory Alex Balatsoukas-Stimming Technical University of Crete May 29, 2010 Telecommunications Laboratory (TUC) Distributed Source Coding
More informationError Detection, Correction and Erasure Codes for Implementation in a Cluster File-system
Error Detection, Correction and Erasure Codes for Implementation in a Cluster File-system Steve Baker December 6, 2011 Abstract. The evaluation of various error detection and correction algorithms and
More informationCoding for loss tolerant systems
Coding for loss tolerant systems Workshop APRETAF, 22 janvier 2009 Mathieu Cunche, Vincent Roca INRIA, équipe Planète INRIA Rhône-Alpes Mathieu Cunche, Vincent Roca The erasure channel Erasure codes Reed-Solomon
More informationPrivacy and Fault-Tolerance in Distributed Optimization. Nitin Vaidya University of Illinois at Urbana-Champaign
Privacy and Fault-Tolerance in Distributed Optimization Nitin Vaidya University of Illinois at Urbana-Champaign Acknowledgements Shripad Gade Lili Su argmin x2x SX i=1 i f i (x) Applications g f i (x)
More informationAsynchronous Models For Consensus
Distributed Systems 600.437 Asynchronous Models for Consensus Department of Computer Science The Johns Hopkins University 1 Asynchronous Models For Consensus Lecture 5 Further reading: Distributed Algorithms
More informationCS505: Distributed Systems
Department of Computer Science CS505: Distributed Systems Lecture 10: Consensus Outline Consensus impossibility result Consensus with S Consensus with Ω Consensus Most famous problem in distributed computing
More informationEE 229B ERROR CONTROL CODING Spring 2005
EE 229B ERROR CONTROL CODING Spring 2005 Solutions for Homework 1 1. Is there room? Prove or disprove : There is a (12,7) binary linear code with d min = 5. If there were a (12,7) binary linear code with
More informationDecomposition Methods for Large Scale LP Decoding
Decomposition Methods for Large Scale LP Decoding Siddharth Barman Joint work with Xishuo Liu, Stark Draper, and Ben Recht Outline Background and Problem Setup LP Decoding Formulation Optimization Framework
More informationInformation redundancy
Information redundancy Information redundancy add information to date to tolerate faults error detecting codes error correcting codes data applications communication memory p. 2 - Design of Fault Tolerant
More informationEarly consensus in an asynchronous system with a weak failure detector*
Distrib. Comput. (1997) 10: 149 157 Early consensus in an asynchronous system with a weak failure detector* André Schiper Ecole Polytechnique Fe dérale, De partement d Informatique, CH-1015 Lausanne, Switzerland
More informationCommunications II Lecture 9: Error Correction Coding. Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved
Communications II Lecture 9: Error Correction Coding Professor Kin K. Leung EEE and Computing Departments Imperial College London Copyright reserved Outline Introduction Linear block codes Decoding Hamming
More informationLecture 12. Block Diagram
Lecture 12 Goals Be able to encode using a linear block code Be able to decode a linear block code received over a binary symmetric channel or an additive white Gaussian channel XII-1 Block Diagram Data
More informationEntanglement and information
Ph95a lecture notes for 0/29/0 Entanglement and information Lately we ve spent a lot of time examining properties of entangled states such as ab è 2 0 a b è Ý a 0 b è. We have learned that they exhibit
More informationByzantine Agreement in Polynomial Expected Time
Byzantine Agreement in Polynomial Expected Time [Extended Abstract] Valerie King Dept. of Computer Science, University of Victoria P.O. Box 3055 Victoria, BC, Canada V8W 3P6 val@cs.uvic.ca ABSTRACT In
More informationGroup Secret Key Agreement over State-Dependent Wireless Broadcast Channels
Group Secret Key Agreement over State-Dependent Wireless Broadcast Channels Mahdi Jafari Siavoshani Sharif University of Technology, Iran Shaunak Mishra, Suhas Diggavi, Christina Fragouli Institute of
More informationDiscrete Mathematics for CS Spring 2007 Luca Trevisan Lecture 11. Error Correcting Codes Erasure Errors
CS 70 Discrete Mathematics for CS Spring 2007 Luca Trevisan Lecture 11 Error Correcting Codes Erasure Errors We will consider two situations in which we wish to transmit information on an unreliable channel.
More informationA Piggybacking Design Framework for Read- and- Download- efficient Distributed Storage Codes. K. V. Rashmi, Nihar B. Shah, Kannan Ramchandran
A Piggybacking Design Framework for Read- and- Download- efficient Distributed Storage Codes K V Rashmi, Nihar B Shah, Kannan Ramchandran Outline IntroducGon & MoGvaGon Measurements from Facebook s Warehouse
More informationOptimal Error Rates for Interactive Coding II: Efficiency and List Decoding
Optimal Error Rates for Interactive Coding II: Efficiency and List Decoding Mohsen Ghaffari MIT ghaffari@mit.edu Bernhard Haeupler Microsoft Research haeupler@cs.cmu.edu Abstract We study coding schemes
More informationBroadcast and Verifiable Secret Sharing: New Security Models and Round-Optimal Constructions
Broadcast and Verifiable Secret Sharing: New Security Models and Round-Optimal Constructions Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in
More informationCOMPSCI 650 Applied Information Theory Apr 5, Lecture 18. Instructor: Arya Mazumdar Scribe: Hamed Zamani, Hadi Zolfaghari, Fatemeh Rezaei
COMPSCI 650 Applied Information Theory Apr 5, 2016 Lecture 18 Instructor: Arya Mazumdar Scribe: Hamed Zamani, Hadi Zolfaghari, Fatemeh Rezaei 1 Correcting Errors in Linear Codes Suppose someone is to send
More informationUniform consensus is harder than consensus
R Available online at www.sciencedirect.com Journal of Algorithms 51 (2004) 15 37 www.elsevier.com/locate/jalgor Uniform consensus is harder than consensus Bernadette Charron-Bost a, and André Schiper
More informationREED-SOLOMON CODE SYMBOL AVOIDANCE
Vol105(1) March 2014 SOUTH AFRICAN INSTITUTE OF ELECTRICAL ENGINEERS 13 REED-SOLOMON CODE SYMBOL AVOIDANCE T Shongwe and A J Han Vinck Department of Electrical and Electronic Engineering Science, University
More informationError Correction Review
Error Correction Review A single overall parity-check equation detects single errors. Hamming codes used m equations to correct one error in 2 m 1 bits. We can use nonbinary equations if we create symbols
More informationDistributed Consensus
Distributed Consensus Reaching agreement is a fundamental problem in distributed computing. Some examples are Leader election / Mutual Exclusion Commit or Abort in distributed transactions Reaching agreement
More informationIterative Quantization. Using Codes On Graphs
Iterative Quantization Using Codes On Graphs Emin Martinian and Jonathan S. Yedidia 2 Massachusetts Institute of Technology 2 Mitsubishi Electric Research Labs Lossy Data Compression: Encoding: Map source
More informationSTUDY OF PERMUTATION MATRICES BASED LDPC CODE CONSTRUCTION
EE229B PROJECT REPORT STUDY OF PERMUTATION MATRICES BASED LDPC CODE CONSTRUCTION Zhengya Zhang SID: 16827455 zyzhang@eecs.berkeley.edu 1 MOTIVATION Permutation matrices refer to the square matrices with
More informationRealistic Failures in Secure Multi-Party Computation
Realistic Failures in Secure Multi-Party Computation Vassilis Zikas 1, Sarah Hauser 2, and Ueli Maurer 1 Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland 1 {vzikas,maurer}@inf.ethz.ch,
More informationNetwork Coding and Schubert Varieties over Finite Fields
Network Coding and Schubert Varieties over Finite Fields Anna-Lena Horlemann-Trautmann Algorithmics Laboratory, EPFL, Schweiz October 12th, 2016 University of Kentucky What is this talk about? 1 / 31 Overview
More informationPolynomial Codes over Certain Finite Fields
Polynomial Codes over Certain Finite Fields A paper by: Irving Reed and Gustave Solomon presented by Kim Hamilton March 31, 2000 Significance of this paper: Introduced ideas that form the core of current
More informationLecture 8: Shannon s Noise Models
Error Correcting Codes: Combinatorics, Algorithms and Applications (Fall 2007) Lecture 8: Shannon s Noise Models September 14, 2007 Lecturer: Atri Rudra Scribe: Sandipan Kundu& Atri Rudra Till now we have
More informationPERFECT SECRECY AND ADVERSARIAL INDISTINGUISHABILITY
PERFECT SECRECY AND ADVERSARIAL INDISTINGUISHABILITY BURTON ROSENBERG UNIVERSITY OF MIAMI Contents 1. Perfect Secrecy 1 1.1. A Perfectly Secret Cipher 2 1.2. Odds Ratio and Bias 3 1.3. Conditions for Perfect
More informationSecure Multi-Party Computation. Lecture 17 GMW & BGW Protocols
Secure Multi-Party Computation Lecture 17 GMW & BGW Protocols MPC Protocols MPC Protocols Yao s Garbled Circuit : 2-Party SFE secure against passive adversaries MPC Protocols Yao s Garbled Circuit : 2-Party
More information