Primer: Data Compression with built-in Encryption
Autosophy algorithms combine high lossless data compression with unbreakable encryption.
Claude Shannon in 1948 first defined “information” and “communications” in a publication: “A mathematical theory of communication”. In an age of primitive telephone and teletypewriters this theory was quickly adopted as the virtual Bible of communication designers and the evolving computer technology. The theory is still being taught in our universities even though no biological creature communicates with binary bit and bytes. Meanwhile, our entire communications infrastructure and the programmed data processing computer were built according to that outdated theory. Shannon’s theory has lead modern information technology into a theoretical trap from which it will be difficult to escape.
According to Shannon’s theory all data (text character, pixels, and analog samples) is regarded as “quantities” to be converted into binary bits and bytes for transmission in “meaningless” bit streams. The amount of “information” in a communication is measured in binary digits (bit or bytes) where each digit (bit) may answer a simple yes-no question. This is obviously not the way we humans, or other biological creatures, communicate. Communication without “meaning” is a contradiction in terms.
Once the number of bits in a communication has been determined then removing bits for data compression becomes theoretically impossible. Any attempt of data compression must lead to inevitable data distortions. The higher the compression ratio the lower the data “quality” including data distortions and loss of resolution.
Data encryption, including the official government standard DES, involves bit scrambling using Pseudo Random Number sequences and a key number to initiate the pseudo random number generator. All these codes can be broken by determined efforts and the use of large computers.
Early Data Compression Methods like: Huffman - Fano coding (also known as “entropy” coding), the Ziv-Lempel LZ-77 and LZ-78, and the Lempel-Ziv-Welch LZW coding scheme, use primitive Autosophy-like algorithms.
This all changed when Klaus Holtz is 1974 discovered the Self-assembling Data Networks, which grow like data crystals or data trees in electronic memories to produce true mathematical learning. A new Autosophy Information Theory, published in 1996, is now replacing the old Shannon technologies using self-assembling hyperspace knowledge libraries. This may achieve very high “lossless” data compression, with built-in unbreakable encryption.
The Autosophy Information Theory regards all data as “addresses” in a hyperspace knowledge library. Each transmitted hyperspace address code, called a “tip”, may represent any amount of data; from single text character to whole books, or from a single pixel to whole images and video sequences. “Information” is only that what can be perceived (seen or heard) by the receiver, and which is not already known by the receiver. The purpose of “Information” and “Communication” is to “create new knowledge” in the receiver, in effect teaching it something.
Data encryption uses custom grown hyperspace libraries, which are distributed as Email attachments to all authorized communication parties. The custom libraries are grown by software from data samples, such as old text files or pictures. Once grows the libraries will not change during the transmission process. Without a matching hyperspace library data interception by unauthorized parties is truly impossible.
Available downloadable references:
1975 Self-learning data networks – Invention, first publication, patent
1990 Application example: the V.42bis compression standard in Modems
1996 Data Compression in Network design – Design SuperCon’96 Tutorial
2004 Autosophy Data / Image Compression and Encryption, SPIE 2004, Slide show