AI News, Blockchain from a Data Science Perspective

Blockchain from a Data Science Perspective

Every transaction in the history of that coin’s existence is in plain sight on the face of the coin—and the longer the coin circulates, the harder for the earliest transactions to be erased or altered.

While today’s most common application of blockchain is tracking cryptocurrency transactions, this emergent technology could be harnessed to manage other kinds of data, records, and assets—such as Renewable Energy Certificates, IoT data, intellectual property rights, or election votes, to name a few.

Nakamoto observes that “no mechanism exists to make payments over a communications channel without a trusted party.” Nakamoto’s solution: “an electronic payment system based on cryptographic proof instead of trust.” Transaction validation is key to understanding the inner workings of a blockchain.

In the PoW model, miners run cryptographic algorithms and compete to be the first to bundle incoming transactions into a validated block (think of this for now as a “page” of the distributed ledger).

Moreover, as the number of independent miners and frequency of transactions in the system goes up, the probability of a 51% attack, or a single coalition taking control of the system, theoretically goes down.

A P2P architecture is used to enable emergent consensus, consensus that happens gradually as a result of individual nodes having a complete copy of records that agrees with the records of the majority of the other nodes.

Roughly every 10 minutes (although this validation time can vary considerably), a new block in the chain is created when the latest batch of new transactions in the network is successfully validated.

Each block, once validated, is copied and distributed throughout the network so that the next batch of incoming transactions can be processed based on data the network has documented as valid.

This hash is lower than The lower the target, the higher the difficulty level, since the likelihood of matching a longer string of zeroes is lower, requiring more computing power to run through more and more iterations to find the right combination of characters that will generate the target cryptographic output.

As the new block is propagated, every node that receives it independently validates the hash according to a set of rules to prevent miners that cheat from being rewarded and invalid blocks from being added to the main blockchain.

Someone who attempts going back down the blockchain to change past transactions will have to rehash all the blocks above and expend tremendous computing power to create a fraudulent ledger with hashes that will not match the majority of the network’s distributed record.

A faster block time would make transactions clear faster but lead to more frequent blockchain forks, whereas a slower block time would decrease the number of forks but make settlement slower.

Nakamoto writes that the blockchain will remain “secure as long as honest nodes collectively control more CPU power than any cooperating group of attacker nodes.” In other words, the security of the distributed ledger depends on the inability of any single group to be in control of the majority of computing power in the system.

This high-level summary of how a blockchain works has been focused primarily on Bitcoin, partly because the Bitcoin blockchain is open source and highly documented, but also because Bitcoin is the most widely known real-world experiment with Distributed Ledger Technology.

If blockchain technology takes off, this could potentially result in large amounts of highly structured, anonymized, and authenticated data assets with transparent provenance.

While we don’t know what blockchains will do for data science, acknowledging this still leaves room to consider what data science might do for blockchain as attempts are made to bring this technology to maturity.

Even a high-level overview of the Bitcoin blockchain reveals a complex transaction system developed through consideration of various probabilities and statistics involved in economics and human behavior.

As new blockchain infrastructures are implemented, data science can help guide their design and development as well as assess the impact of blockchain on business processes.

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