Bitcoin is a crypto currency, a distributed peer-to-peer financial system. Well actually it is an electronic system which manages the provisional ownership of a strictly fixed supply of abstract fungible units which really works as a distributed property register or a digital notary service. This is not so different than managing the ownership of shares in traditional financial markets. Modern financial institutions increasingly just do NOT trust each other, they build co-operative robust and decentralized and increasingly transparent, electronic systems which are and able to both serve the diverse objectives of participants (e.g. traders) and uphold certain security policies. Is Bitcoin actually so brilliant to be called the Internet of money as it is sometimes claimed? Not quite. Consider just the question of speed. Super low latency transactions are a norm in the financial industry, and even ordinary people have access to super fast bank transfers and real-time credit card transactions. Bitcoin remains rather the horse carriage of money. In this paper we look at the question of fast transaction acceptance in bitcoin and other crypto currencies. We claim that bitcoin needs to change in order to be able to satisfy the most basic needs of modern users.

Industry-standard lossless compression algorithms (such as LZW) are usually implemented so that they work on bytes as symbols. Experiments indicate that data for which bytes are not the natural choice of symbols compress poorly using these implementations, while algorithms working on a bit level perform reasonably on byte-based data in addition to having computational advantages resulting from operating on a small alphabet. In this correspondence, we offer an information-theoretic explanation to these experimental results by assessing the redundancy (which is approximated by the divergence rate of two source distributions) of a bit-based model when applied to a byte-based source. More specifically, we study the problem of approximating a block Markov source (our model for byte-based data) with higher order Markov sources (which model bit-based Markov encoders), and show that the divergence rate between a block Markov source and the best matching higher order Markov model for that source converges to zero exponentially fast as the memory of the model increases. This result is applied to obtain bounds on the redundancy of certain symbol-based universal codes when they are used for byte-aligned sources

Summary form only given. An experiment was conducted to evaluate a block-sorting compression scheme that operates at the bit level. The experiment demonstrated that even such a simple technique, which ignores byte boundaries and uses a very simple modeling scheme for the output of the block-sorting transform, outperforms some of the best industry standard compressors for sources that are not byte-aligned, while providing reasonable compression ratios for byte-aligned sources. Although the scheme can be substantially improved using more sophisticated modeling and coding techniques, preliminary experimental results point out the potential advantages of this approach.