Crypto

Cryptography is a funny business; shady from the beginning, the whole business of codes and ciphers has been specifically designed to hide your intentions and move in the shadows, unnoticed. However, the art of cryptography has been changed almost beyond recognition in the last hundred years thanks to the invention of the computer, and what was once an art limited by the imagination of the nerd responsible has now turned into a question of sheer computing might. But, as always, the best way to start with this story is at the beginning…

There are two different methods of applying cryptography to a message; with a code or with a cipher. A code is a system involving replacing words with other words (‘Unleash a fox’ might mean ‘Send more ammunition’, for example), whilst a cipher involves changing individual letters and their ordering. Use of codes can generally only be limited to a few words that can be easily memorised, and/or requires endless cross-referencing with a book of known ‘translations’, as well as being relatively insecure when it comes to highly secretive information. Therefore, most modern encoding (yes, that word is still used; ‘enciphering’ sounds stupid) takes the form of employing ciphers, and has done for hundreds of years; they rely solely on the application of a simple rule, require far smaller reference manuals, and are more secure.

Early attempts at ciphers were charmingly simple; the ‘Caesar cipher’ is a classic example, famously invented and used by Julius Caesar, where each letter is replaced by the one three along from it in the alphabet (so A becomes D, B becomes E and so on). Augustus Caesar, who succeeded Julius, didn’t set much store by cryptography and used a similar system, although with only a one-place transposition (so A to B and such)- despite the fact that knowledge of the Caesar cipher was widespread, and his messages were hopelessly insecure. These ‘substitution ciphers’ suffered from a common problem; the relative frequency with which certain letters appear in the English language (E being the most common, followed by T) is well-known, so by analysing the frequency of occurring letters in a substitution-enciphered message one can work out fairly accurately what letter corresponds to which, and work out the rest from there. This problem can be partly overcome by careful phrasing of messages and using only short ones, but it’s nonetheless a problem.

Another classic method is to use a transposition cipher, which changes the order of letters- the trick lies in having a suitable ‘key’ with which to do the reordering. A classic example is to write the message in a rectangle of a size known to both encoder and recipient, writing in columns but ‘reading it off’ in rows. The recipient can then reverse the process to read the original message. This is a nice method, and it’s very hard to decipher a single message encoded this way, but if the ‘key’ (e.g. the size of the rectangle) is not changed regularly then one’s adversaries can figure it out after a while. The army of ancient Sparta used a kind of transposition cipher based on a tapered wooden rod called a skytale (pronounced skih-tah-ly), around which a strip of paper was wrapped and the message written down it, one on each turn of paper. The recipient then wrapped the paper around a skytale of identical girth and taper (the tapering prevented letters being evenly spaced, making it harder to decipher), and read the message off- again, a nice idea, but the need to make a new set of skytale’s for everyone every time the key needed changing rendered it impractical. Nonetheless, transposition ciphers are a nice idea, and the Union used them to great effect during the American Civil War.

In the last century, cryptography has developed into even more of an advanced science, and most modern ciphers are based on the concept of transposition ciphers- however, to avoid the problem of using letter frequencies to work out the key, modern ciphers use intricate and elaborate systems to change by how much the ‘value’ of the letter changes each time. The German Lorenz cipher machine used during the Second World War (and whose solving I have discussed in a previous post) involved putting the message through three wheels and electronic pickups to produce another letter; but the wheels moved on one click after each letter was typed, totally changing the internal mechanical arrangement. The only way the British cryptographers working against it could find to solve it was through brute force, designing a computer specifically to test every single possible starting position for the wheels against likely messages. This generally took them several hours to work out- but if they had had a computer as powerful as the one I am typing on, then provided it was set up in the correct manner it would have the raw power to ‘solve’ the day’s starting positions within a few minutes. Such is the power of modern computers, and against such opponents must modern cryptographers pit themselves.

One technique used nowadays presents a computer with a number that is simply too big for it to deal with; they are called ‘trapdoor ciphers’. The principle is relatively simple; it is far easier to find that 17 x 19 = 323 than it is to find the prime factors of 323, even with a computer, so if we upscale this business to start dealing with huge numbers a computer will whimper and hide in the corner just looking at them. If we take two prime numbers, each more than 100 digits long (this is, by the way, the source of the oft-quoted story that the CIA will pay $10,000 to anyone who finds a prime number of over 100 digits due to its intelligence value) and multiply them together, we get a vast number with only two prime factors which we shall, for now, call M. Then, we convert our message into number form (so A=01, B=02, I LIKE TRAINS=0912091105201801091419) and the resulting number is then raised to the power of a third (smaller, three digits will do) prime number. This will yield a number somewhat bigger than M, and successive lots of M are then subtracted from it until it reaches a number less than M (this is known as modulo arithmetic, and can be best visualised by example: so 19+16=35, but 19+16 (mod 24)=11, since 35-24=11). This number is then passed to the intended recipient, who can decode it relatively easily (well, so long as they have a correctly programmed computer) if they know the two prime factors of M (this business is actually known as the RSA problem, and for reasons I cannot hope to understand current mathematical thinking suggests that finding the prime factors of M is the easiest way of solving this; however, this has not yet been proven, and the matter is still open for debate). However, even if someone trying to decode the message knows M and has the most powerful computer on earth, it would take him thousands of years to find out what its prime factors are. To many, trapdoor ciphers have made cryptoanalysis (the art of breaking someone else’s codes), a dead art.

Man, there’s a ton of cool crypto stuff I haven’t even mentioned yet… screw it, this is going to be a two-parter. See you with it on Wednesday…

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Getting bored with history lessons

Last post’s investigation into the post-Babbage history of computers took us up to around the end of the Second World War, before the computer age could really be said to have kicked off. However, with the coming of Alan Turing the biggest stumbling block for the intellectual development of computing as a science had been overcome, since it now clearly understood what it was and where it was going. From then on, therefore, the history of computing is basically one long series of hardware improvements and business successes, and the only thing of real scholarly interest was Moore’s law. This law is an unofficial, yet surprisingly accurate, model of the exponential growth in the capabilities of computer hardware, stating that every 18 months computing hardware gets either twice as powerful, half the size, or half the price for the same other specifications. This law was based on a 1965 paper by Gordon E Moore, who noted that the number of transistors on integrated circuits had been doubling every two years since their invention 7 years earlier. The modern day figure of an 18-monthly doubling in performance comes from an Intel executive’s estimate based on both the increasing number of transistors and their getting faster & more efficient… but I’m getting sidetracked. The point I meant to make was that there is no point me continuing with a potted history of the last 70 years of computing, so in this post I wish to get on with the business of exactly how (roughly fundamentally speaking) computers work.

A modern computer is, basically, a huge bundle of switches- literally billions of the things. Normal switches are obviously not up to the job, being both too large and requiring an electromechanical rather than purely electrical interface to function, so computer designers have had to come up with electrically-activated switches instead. In Colossus’ day they used vacuum tubes, but these were large and prone to breaking so, in the late 1940s, the transistor was invented. This is a marvellous semiconductor-based device, but to explain how it works I’m going to have to go on a bit of a tangent.

Semiconductors are materials that do not conduct electricity freely and every which way like a metal, but do not insulate like a wood or plastic either- sometimes they conduct, sometimes they don’t. In modern computing and electronics, silicon is the substance most readily used for this purpose. For use in a transistor, silicon (an element with four electrons in its outer atomic ‘shell’) must be ‘doped’ with other elements, meaning that they are ‘mixed’ into the chemical, crystalline structure of the silicon. Doping with a substance such as boron, with three electrons in its outer shell, creates an area with a ‘missing’ electron, known as a hole. Holes have, effectively, a positive charge compared a ‘normal’ area of silicon (since electrons are negatively charged), so this kind of doping produces what is known as p-type silicon. Similarly, doping with something like phosphorus, with five outer shell electrons, produces an excess of negatively-charged electrons and n-type silicon. Thus electrons, and therefore electricity (made up entirely of the net movement of electrons from one area to another) finds it easy to flow from n- to p-type silicon, but not very well going the other way- it conducts in one direction and insulates in the other, hence a semiconductor. However, it is vital to remember that the p-type silicon is not an insulator and does allow for free passage of electrons, unlike pure, undoped silicon. A transistor generally consists of three layers of silicon sandwiched together, in order NPN or PNP depending on the practicality of the situation, with each layer of the sandwich having a metal contact or ‘leg’ attached to it- the leg in the middle is called the base, and the ones at either side are called the emitter and collector.

Now, when the three layers of silicon are stuck next to one another, some of the free electrons in the n-type layer(s) jump to fill the holes in the adjacent p-type, creating areas of neutral, or zero, charge. These are called ‘depletion zones’ and are good insulators, meaning that there is a high electrical resistance across the transistor and that a current cannot flow between the emitter and collector despite usually having a voltage ‘drop’ between them that is trying to get a current flowing. However, when a voltage is applied across the collector and base a current can flow between these two different types of silicon without a problem, and as such it does. This pulls electrons across the border between layers, and decreases the size of the depletion zones, decreasing the amount of electrical resistance across the transistor and allowing an electrical current to flow between the collector and emitter. In short, one current can be used to ‘turn on’ another.

Transistor radios use this principle to amplify the signal they receive into a loud, clear sound, and if you crack one open you should be able to see some (well, if you know what you’re looking for). However, computer and manufacturing technology has got so advanced over the last 50 years that it is now possible to fit over ten million of these transistor switches onto a silicon chip the size of your thumbnail- and bear in mind that the entire Colossus machine, the machine that cracked the Lorenz cipher, contained only ten thousand or so vacuum tube switches all told. Modern technology is a wonderful thing, and the sheer achievement behind it is worth bearing in mind next time you get shocked over the price of a new computer (unless you’re buying an Apple- that’s just business elitism).

…and dammit, I’ve filled up a whole post again without getting onto what I really wanted to talk about. Ah well, there’s always next time…

(In which I promise to actually get on with talking about computers)

A Continued History

This post looks set to at least begin by following on directly from my last one- that dealt with the story of computers up to Charles Babbage’s difference and analytical engines, whilst this one will try to follow the history along from there until as close to today as I can manage, hopefully getting in a few of the basics of the workings of these strange and wonderful machines.

After Babbage’s death as a relatively unknown and unloved mathematician in 1871, the progress of the science of computing continued to tick over. A Dublin accountant named Percy Ludgate, independently of Babbage’s work, did develop his own programmable, mechanical computer at the turn of the century, but his design fell into a similar degree of obscurity and hardly added anything new to the field. Mechanical calculators had become viable commercial enterprises, getting steadily cheaper and cheaper, and as technological exercises were becoming ever more sophisticated with the invention of the analogue computer. These were, basically a less programmable version of the difference engine- mechanical devices whose various cogs and wheels were so connected up that they would perform one specific mathematical function to a set of data. James Thomson in 1876 built the first, which could solve differential equations by integration (a fairly simple but undoubtedly tedious mathematical task), and later developments were widely used to collect military data and for solving problems concerning numbers too large to solve by human numerical methods. For a long time, analogue computers were considered the future of modern computing, but since they solved and modelled problems using physical phenomena rather than data they were restricted in capability to their original setup.

A perhaps more significant development came in the late 1880s, when an American named Herman Hollerith invented a method of machine-readable data storage in the form of cards punched with holes. These had been around for a while to act rather like programs, such as the holed-paper reels of a pianola or the punched cards used to automate the workings of a loom, but this was the first example of such devices being used to store data (although Babbage had theorised such an idea for the memory systems of his analytical engine). They were cheap, simple, could be both produced and read easily by a machine, and were even simple to dispose of. Hollerith’s team later went on to process the data of the 1890 US census, and would eventually become most of IBM. The pattern of holes on these cards could be ‘read’ by a mechanical device with a set of levers that would go through a hole if there was one present, turning the appropriate cogs to tell the machine to count up one. This system carried on being used right up until the 1980s on IBM systems, and could be argued to be the first programming language.

However, to see the story of the modern computer truly progress we must fast forward to the 1930s. Three interesting people and acheivements came to the fore here: in 1937 George Stibitz, and American working in Bell Labs, built an electromechanical calculator that was the first to process data digitally using on/off binary electrical signals, making it the first digital. In 1936, a bored German engineering student called Konrad Zuse dreamt up a method for processing his tedious design calculations automatically rather than by hand- to this end he devised the Z1, a table-sized calculator that could be programmed to a degree via perforated film and also operated in binary. His parts couldn’t be engineered well enough for it to ever work properly, but he kept at it to eventually build 3 more models and devise the first programming language. However, perhaps the most significant figure of 1930s computing was a young, homosexual, English maths genius called Alan Turing.

Turing’s first contribution to the computing world came in 1936, when he published a revolutionary paper showing that certain computing problems cannot be solved by one general algorithm. A key feature of this paper was his description of a ‘universal computer’, a machine capable of executing programs based on reading and manipulating a set of symbols on a strip of tape. The symbol on the tape currently being read would determine whether the machine would move up or down the strip, how far, and what it would change the symbol to, and Turing proved that one of these machines could replicate the behaviour of any computer algorithm- and since computers are just devices running algorithms, they can replicate any modern computer too. Thus, if a Turing machine (as they are now known) could theoretically solve a problem, then so could a general algorithm, and vice versa if it couldn’t. Not only that, but since modern computers cannot multi-task on the. These machines not only lay the foundations for computability and computation theory, on which nearly all of modern computing is built, but were also revolutionary as they were the first theorised to use the same medium for both data storage and programs, as nearly all modern computers do. This concept is known as a von Neumann architecture, after the man who first pointed out and explained this idea in response to Turing’s work.

Turing machines contributed one further, vital concept to modern computing- that of Turing-completeness. A Turing-complete system was defined as a single Turing machine (known as a Universal Turing machine) capable of replicating the behaviour of any other theoretically possible Turing machine, and thus any possible algorithm or computable sequence. Charles Babbage’s analytical engine would have fallen into that class had it ever been built, in part because it was capable of the ‘if X then do Y’ logical reasoning that characterises a computer rather than a calculator. Ensuring the Turing-completeness of a system is a key part of designing a computer system or programming language to ensure its versatility and that it is capable of performing all the tasks that could be required of it.

Turing’s work had laid the foundations for nearly all the theoretical science of modern computing- now all the world needed was machines capable of performing the practical side of things. However, in 1942 there was a war on, and Turing was being employed by the government’s code breaking unit at Bletchley Park, Buckinghamshire. They had already cracked the German’s Enigma code, but that had been a comparatively simple task since they knew the structure and internal layout of the Enigma machine. However, they were then faced by a new and more daunting prospect: the Lorenz cipher, encoded by an even more complex machine for which they had no blueprints. Turing’s genius, however, apparently knew no bounds, and his team eventually worked out its logical functioning. From this a method for deciphering it was formulated, but it required an iterative process that took hours of mind-numbing calculation to get a result out. A faster method of processing these messages was needed, and to this end an engineer named Tommy Flowers designed and built Colossus.

Colossus was a landmark of the computing world- the first electronic, digital, and partially programmable computer ever to exist. It’s mathematical operation was not highly sophisticated- it used vacuum tubes containing light emission and sensitive detection systems, all of which were state-of-the-art electronics at the time, to read the pattern of holes on a paper tape containing the encoded messages, and then compared these to another pattern of holes generated internally from a simulation of the Lorenz machine in different configurations. If there were enough similarities (the machine could obviously not get a precise matching since it didn’t know the original message content) it flagged up that setup as a potential one for the message’s encryption, which could then be tested, saving many hundreds of man-hours. But despite its inherent simplicity, its legacy is simply one of proving a point to the world- that electronic, programmable computers were both possible and viable bits of hardware, and paved the way for modern-day computing to develop.