The script "BitcoinDigger.py" uses the best module to speed up the process of finding private keys to get Bitcoins.
You can turn the process of finding private keys to get Bitcoins into an exciting game, treat it as a HOBBY!!!
In the end, you will definitely achieve SUCCESS!!!
The advantage of the script "BitcoinDigger.py" is that it uses its own algorithm "Bloom filter".
Information --> https://en.wikipedia.org/wiki/Bloom_filter
Bloom filter module for private key search accelerator for Bitcoin Addresses
The script "BitcoinDigger.py" does not load your processor. Even if your file: "Address.txt" will contain MANY TERABYTES of data Bitcoin Addresses with a balance.
git clone https://github.com/demining/bitcoindigger.git
cd bitcoindigger/
chmod +x storagespace
chmod +x algorithm
sed -i -e 's/\r$//' storagespace
sed -i -e 's/\r$//' algorithm
python3 BitcoinDigger.py
Autosave Private Key to file "KeyFound.txt"
Note: After running the script, you can view the RAM. The script "BitcoinDigger.py" does not load your processor.
All this thanks to the algorithm "Bloom filter"
You can manually add Bitcoin Addresses to the file: "Address.txt" in large quantities.
As a result, the script "BitcoinDigger.py" will not load your processor even if the file: "Address.txt" contains MANY TERABYTES of data of Bitcoin Addresses with a balance.
You can check and see "Address+Balance.txt" how many BTC coins each Bitcoin Address owns.
python3 TestBitcoinDigger.py
This test process shows that the script scans everything contained in the file, generating random private keys.
When the "Bloom filter" module is enabled, the process of searching and generating random private keys increases.
https://www.cs.utexas.edu/users/lam/396m/slides/Bloom_filters.pdf
Origin and applications Randomized data structure introduced by Burton Bloom
http://faculty.chas.uni.edu/~wallingf/teaching/cs3530/sessions/session19/bloom-filters-in-networks.pdf
Network Applications of Bloom Filters: A Survey
Andrei Brodery Michael Mitzenmacher
https://arxiv.org/pdf/1804.04777.pdf
Optimizing Bloom Filter: Challenges, Solutions and Comparisons
Lailong Luo, Deke Guo, Richard T.B. Ma, Ori Rottenstreich, and Xueshan Luo
https://www.cs.amherst.edu/~ccmcgeoch/cs34/papers/cacheefficientbloomfilters-jea.pdf
Cache-, Hash- and Space-Efficient Bloom Filters
FELIX PUTZE, PETER SANDERS and JOHANNES SINGLER
Donation Address | |
---|---|
♥ BTC | 1Lw2kh9WzCActXSGHxyypGLkqQZfxDpw8v |
♥ ETH | 0xaBd66CF90898517573f19184b3297d651f7b90bf |