BigDb is an example of how to exponentially increase a database of sequentially ordered public keys.

build

On Windows, use WSL.

make

A database of sequential points is created and stored in a Bloom filter.

Creating the Database

To create a database with 1,000,000 points:

./BigDb -bs 1000000

Once created, a log file is generated that records the point count. In this case, the log file will record 1,000,000 points. This log file serves as a guide both for expanding the database and for performing accurate searches.

Example of Expanding the Database

If we want to add 1,000,000 more points:

./BigDb -bs 1000000

This will start creating points from where the log file indicates. For our example, points will be created from 1,000,000 to 2,000,000 (because we are adding 1,000,000 points).

Using the Database

Once our database is created, we run:

./BigDb -f 30.txt -ht 1000

The -f parameter will read from the "30.txt" file the search range and the target public key, and will create a hash table with the -ht parameter.

The hash table is composed as follows:

It reads the log file to know the size of our database.

It subtracts this amount (2,000,000 in the example) from the target the number of times indicated by -ht (1,000 in the example).

This subtracts 2,000,000 from the public key 1,000 times consecutively, temporarily storing all intermediate results in the hash table.

We have increased the range covered by the database by 1,000 times to 2,000,000 * 1,000 = 2,000,000,000.

This improvement requires extra computation power for the search (explained later).

Search

For the search in this example, we use traditional brute force:

A random number is created within the search range.

This random point is searched by finding the difference with each element of the hash table by subtracting.

If the random point is within the range "target - 2,000,000,000:target", eventually one of those results will be in the Bloom filter and we can easily calculate the private key.

Although we could exponentially increase our database (x10, x100, x1000... x1,000,000), the computational load also increases. In our example, it would add 1,000 additional subtractions.

Thanks for reading. This example is not intended for actual use; the code is just a poorly optimized example.

Donate to:

btc: bc1qxs47ttydl8tmdv8vtygp7dy76lvayz3r6rdahu