Coding Theory and Cryptography: Error Correction and Information Security, Exams of Communication

Download Coding Theory and Cryptography: Error Correction and Information Security and more Exams Communication in PDF only on Docsity! What is Coding Theory and What is Cryptography? The term coding is an overloaded and sometimes misunderstood term. Basically, there are three areas the term coding is associated with. 1. Data Compression: concerned with efficient encoding of source information so that it takes as little space as possible. This is possible by removing redundancy from the data. Source encoding is a part of Information Theory and we won’t be dealing with it in this course. 2. Error-Correcting Codes: concerned with improving reliability of communication over noisy channels. This is achieved by adding redundancy. 3. Cryptography (or Cryptology) is concerned with security, privacy or confidentiality of communication over an insecure channel. Over the past few decades, the term “coding theory” has become associated predominantly with error correcting codes. A good part of this course will be devoted to coding theory. It is interesting to note that whereas cryptography strives to render data unintelligible to all but the intended recipient, error-correcting codes attempt to ensure data is decodable despite any disrup- tions introduced by the medium. Data compression and error correction also contrast one another in that the former involves compaction and the latter data expansion. Question: Does error correction take place in human communication? Written or oral? Suppose you read ”whal a gmeat course”. Basic Problem of Coding Theory Messages are transmitted over a communication channel which is subject to noise. Noise can distort messages Goals: “Error detection” “Error correction” Question: How can we achieve these goals, efficiently? Simple Examples I) Duplicate the message to be sent. 1011 → 10111011 What can we say about error-detection or correction ability of this code? Cost: Rate = ? II) Add a parity check, so that there are even number of 1’s, or sum of digits is 0 mod 2. 1011 → 10111 Error detection/correction? Cost: Rate = ? III) Repeat the original messages three times 1011 → 101110111011 Error detection/correction ability? Cost: Rate = ? Question: Do you see two competing goals here? A trade off? Transmission Process of a Message source - encoder - channel - received message - decoder - receiver 1011 1011010 noise 1010010 1011010 1011 Applications and Some History of Coding Theory Error Correcting Codes have a wide range of applications. Here is a list of some of the applications • Transmission of pictures from distant space • Quality of sound in CD’s: “Reed-Solomon” codes are used in CD’s. • Telephone lines, computer networks • Wireless communication • Universal Product Codes, ISBN numbers • Most recently, quantum error-correction • Their uses are ever expanding The beginning: Claude Shannon’s 1948 paper “A Mathematical Theory of Communication” marks the birth of a new subject called “Information Theory”, part of which is coding theory. He estab- lished the theoretical foundations of the subject. He showed that “good codes” (we will see in this course what that means) exist without showing them! His prove was probabilistic and existential, not constructive. It remained a big challenge to construct and implement efficient codes for a long time. Richard Hamming was one of the first to actually construct and implement error correcting codes. He did this out of frustration he had due to Bell Lab’s mechanical relay computer’s inability to deal with errors. He said “Damn it, if the machine can detect an error, why can’t it locate the position and correct it?”. There is a class of codes known as Hamming Codes which we are going to study. 1965: Mariner 4 was the first spaceship to photograph another planet, taking 22 complete pho- tographs of Mars. Each picture was broken down into 200 × 200 picture elements. Each element was assigned a binary 6-tuple representing one of 64 brightness levels from white (000000) to black (111111). Thus the total number of bits per picture was 240 000. Data was transmitted at the rate of 8.33 bits per second, so it took 8 hours to transmit a single picture! 1969-72: Much improved pictures of Mars were obtained by Mariners 6,7, and 9 (Mariner 8 was lost during launching). One reason for the improvement was the use of a powerful error-correcting code known as (32,64,16) Reed-Muller code. In this code a binary 6-tuple representing the brightness of a dot in the picture was encoded as binary codeword of length 32. The data transmission rate was increased from 8.33 to 16200 bits per second. 1976: Viking 1 landed on Mars and returned high quality color photographs. Surprisingly, the transmission of a color picture in the form of a binary data is almost as easy as transmission of a black-and-white one. 1979: High resolution color pictures of Jupiter and its moons were returned by Voyager 1.