Maintaining the ledger. What are private keys? Accessing the system. How do I keep my Bitcoin and other cryptocurrencies safe? Recognising scams to reduce risks. What are the risks with Bitcoin and other cryptocurrencies? With potential comes risk. How do I protect my private keys? Wallets, vaults and private keys. What are smart contracts? A way to connect, automate and eliminate inefficiency. What is Bitcoin?
6. The Bitcoin Network - Mastering Bitcoin [Book]
The basics of a financial revolution. Where did Bitcoin come from? The origins of a trustless system. Bitcoin as a payment system Consumer and merchant benefits. What is Ethereum? The first global computer. Where did Ethereum come from? A more versatile blockchain. What is the difference between Bitcoin and Ethereum? Bitcoin and Ethereum have very different purposes? What is Bitcoin Cash?
A peer-to-peer payments currency. Where did Bitcoin Cash come from? Bitcoin Cash came from Bitcoin. What is the difference between Bitcoin and Bitcoin Cash?
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Bitcoin and Bitcoin Cash have different priorities. What is Ripple and XRP? Crypto for cross-border payments. Where did XRP come from? XRP was integrated into Ripple in What is the difference between Bitcoin and XRP? XRP and Bitcoin offer different functionality. What is Litecoin? A faster version of Bitcoin.
Where did Litecoin come from? A fork of the Bitcoin Core client.
Peer-to-Peer Network Architecture
What is the difference between Bitcoin and Litecoin? Litecoin aims to improve fees and transaction times. Got it. Bloom filters are implemented as a variable-size array of N binary digits a bit field and a variable number of M hash functions. The hash functions are designed to always produce an output that is between 1 and N, corresponding to the array of binary digits. The hash functions are generated deterministically, so that any node implementing a bloom filter will always use the same hash functions and get the same results for a specific input.
By choosing different length N bloom filters and a different number M of hash functions, the bloom filter can be tuned, varying the level of accuracy and therefore privacy. In Figure , we use a very small array of 16 bits and a set of three hash functions to demonstrate how bloom filters work.
The bloom filter is initialized so that the array of bits is all zeros. To add a pattern to the bloom filter, the pattern is hashed by each hash function in turn. Applying the first hash function to the input results in a number between 1 and N. The corresponding bit in the array indexed from 1 to N is found and set to 1 , thereby recording the output of the hash function.
Then, the next hash function is used to set another bit and so on. Adding a second pattern is as simple as repeating this process. The pattern is hashed by each hash function in turn and the result is recorded by setting the bits to 1. Note that as a bloom filter is filled with more patterns, a hash function result might coincide with a bit that is already set to 1 , in which case the bit is not changed. In essence, as more patterns record on overlapping bits, the bloom filter starts to become saturated with more bits set to 1 and the accuracy of the filter decreases.
This is why the filter is a probabilistic data structure—it gets less accurate as more patterns are added. The accuracy depends on the number of patterns added versus the size of the bit array N and number of hash functions M. A larger bit array and more hash functions can record more patterns with higher accuracy. A smaller bit array or fewer hash functions will record fewer patterns and produce less accuracy.
To test if a pattern is part of a bloom filter, the pattern is hashed by each hash function and the resulting bit pattern is tested against the bit array. If all the bits indexed by the hash functions are set to 1 , then the pattern is probably recorded in the bloom filter. Because the bits may be set because of overlap from multiple patterns, the answer is not certain, but is rather probabilistic.
The corresponding bits are set to 1 , so the pattern is probably a match. On the contrary, if a pattern is tested against the bloom filter and any one of the bits is set to 0 , this proves that the pattern was not recorded in the bloom filter.
A negative result is not a probability, it is a certainty. One of the corresponding bits is set to 0 , so the pattern is definitely not a match.
Hacker Infects Node.js Package to Steal from Bitcoin Wallets
See Appendix B or visit GitHub. Bloom filters are used to filter the transactions and blocks containing them that an SPV node receives from its peers.
The SPV node will then send a filterload message to the peer, containing the bloom filter to use on the connection. Only transactions that match the filter are sent to the node. In response to a getdata message from the node, peers will send a merkleblock message that contains only block headers for blocks matching the filter and a merkle path see Merkle Trees for each matching transaction. The peer will then also send tx messages containing the transactions matched by the filter. The node setting the bloom filter can interactively add patterns to the filter by sending a filteradd message.
To clear the bloom filter, the node can send a filterclear message. Because it is not possible to remove a pattern from a bloom filter, a node has to clear and resend a new bloom filter if a pattern is no longer desired. Almost every node on the bitcoin network maintains a temporary list of unconfirmed transactions called the memory pool , or transaction pool. Nodes use this pool to keep track of transactions that are known to the network but are not yet included in the blockchain.
As transactions are received and verified, they are added to the transaction pool and relayed to the neighboring nodes to propagate on the network. Some node implementations also maintain a separate pool of orphaned transactions. Any matching orphans are then validated. If valid, they are removed from the orphan pool and added to the transaction pool, completing the chain that started with the parent transaction.
In light of the newly added transaction, which is no longer an orphan, the process is repeated recursively looking for any further descendants, until no more descendants are found. Through this process, the arrival of a parent transaction triggers a cascade reconstruction of an entire chain of interdependent transactions by re-uniting the orphans with their parents all the way down the chain. Both the transaction pool and orphan pool where implemented are stored in local memory and are not saved on persistent storage; rather, they are dynamically populated from incoming network messages.
When a node starts, both pools are empty and are gradually populated with new transactions received on the network. Unlike the transaction and orphan pools, the UTXO pool is not initialized empty but instead contains millions of entries of unspent transaction outputs, including some dating back to The UTXO pool may be housed in local memory or as an indexed database table on persistent storage. Furthermore, the transaction and orphan pools only contain unconfirmed transactions, while the UTXO pool only contains confirmed outputs.
Alert messages are a seldom used function, but are nevertheless implemented in most nodes. This feature is implemented to allow the core developer team to notify all bitcoin users of a serious problem in the bitcoin network, such as a critical bug that requires user action.