Does anybody has this script, its gone. What to use to look at the DP's I have in the workfiles?
If you can compile your own version, you can add the export option that https://github.com/PatatasFritas/FriedKangaroo created. If you search for PatatasFritas in this thread, they also have a python script that will do the same; export the wild and tame files. This was previously posted in this thread but deleted for some reason, it's a snippet from iceland2k14's Github that divides a pubkey by an arbitrary number and returning all the parts in between. I polished it a bit to print the compressed and uncompressed keys: https://gist.github.com/ZenulAbidin/286a652b160086b3b0f184a886ba68caHere's the script output when called with a random (uninteresting - with no balance) pubkey, divided by 48. The keys divided by 0, 1, 2, 3 and so on are printed in order: >>> shiftdown("0267830f83723fa565ccb8677c14d13f7d47bf1008fea826833359f27f866632ab", 48)
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022c5062793956267056d8420aff1e7628533cf9d0e4d30416aea06c8ffa57075a 042c5062793956267056d8420aff1e7628533cf9d0e4d30416aea06c8ffa57075ae55c7e00e8d3502960abc07df0462bac678d50fa0283423b6501c5cf384b4292
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032a9c89f590aba176ef0d1a69dab1121fdf4d925900d3fa58676267b02c538567 042a9c89f590aba176ef0d1a69dab1121fdf4d925900d3fa58676267b02c5385672974e58f4b1073c60c89e2e7d1a1d39494cbe10e3fcfa2ed07843c078cc1af07
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036b54187fd9bbbfa4839f0a8cd22117bb25c32d0488005efd2932748ca6b170b4 046b54187fd9bbbfa4839f0a8cd22117bb25c32d0488005efd2932748ca6b170b42776916233d8ebb1e7f8ced8ed49dded5c5431891f4cada816bd09f77ab965b1
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03762c522915bbf095256a81d5e4f727e5de10adeefae46a29aa145245cdfeb1aa 04762c522915bbf095256a81d5e4f727e5de10adeefae46a29aa145245cdfeb1aaf38fe17a77bea18c35f8597a58e3b5581c35ccfe07cd66b61c20b1858c21c905
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What's the link to the divisor script?
and how many keys can I generate with the divisor?
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Do you put a private key or public key in the input.txt because I used 1 public key. When I ran the script it gave me just 1 public key.
Oh you just use public keys. Just change the nkeys variable at the top to be the number of keys you want to receive (and make sure bits_toinspect is greater than or equal to log2(nkeys).) That's equivalent to knowing the very lowest bit of the private key so you've effectively reduced your search range by a power of 2 e.g. 2^60 --- 2^59. It's not useful enough in a practical setting though. Not to mention there isn't a foolproof way to guess the private key's parity in the first place.
Do you have an example of that? |
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:-)
Does it mean that 128 MSB bits in search interval must be fixed?
If yes then what for is it worth?
EDIT:
I tried to put in config file 256 bit range and program was not complaining even shoved range as 2^256
what this limit means exactly??
CORRECTION: it is written in Readme file 125bit (not 128 as I wrote previously) so even more confusing.....
Yea, it is limited to search 125 bit. This person attempted to make it search 256 bit but it needs a little more work to it. https://github.com/ZenulAbidin/Kangaroo-256 |
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Today I sucessfully migrated JLP kangaroo to R1 curve without gpu support to be able to compile on windows.
It was necessary in project C/C++ properties to remove flag WITHGPU. Then it compiled and it works.
I read in readme file that kangaroo is only for 128 bits. What does it mean?
That mean its limited to search 128 bits. |
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What does Std. Deviation: 1.1902380714238083 mean?
That it's likely a human-generated private key. The standard deviations start from 0 to just over +-1, where +-1 (and above) mean that the key is definitely human-generated and 0 means it's definitely computer-generated. None of the keys have std. deviation = 0 because you can never be quite sure whether a computer really did generate a key. But at low std. deviations you can be sure that the private key resembles as much as a random private key as possible. Do you put a private key or public key in the input.txt because I used 1 public key. When I ran the script it gave me just 1 public key. |
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Anyway, this is the script I promised I'd show you guys in my previous post: https://gist.github.com/ZenulAbidin/cbe69f8a2496514773140516e3666519You give it any public key in the file input.txt, adjust the script values such as the number of trailing bits, number of results etc. and it will print you the list of most likely public keys. Warning: bit numbers >20 will cause you to run out of memory fast. Also it might take about an hour or so to complete depending on the bit size. I am working on a fix for both of these. What does Std. Deviation: 1.1902380714238083 mean? |
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Kangaroo is cracking the discrete Logarythm of the pubkey. VanitySearch is looking for Prefixes which are pubkeys sha256 and ripemd160.
How do you want to mix those?
Can you search for an address with vanitygen with a public key? I was thinking would that be able to shorten the search space like kangaroo.
Not 100% sure with vanitygen but vanity search takes inputted address and converts it to its RIPEMD160, then searches for a match for the RIPEMD160. One could tweak code to search for a pubkey which would save one sha256 and the one RIPEMD160 function. Priv key Pub key sha256 ripemd160 so you would save two functions but I am not sure on the speed gained since normally, the most time consuming part. whether its CPU or GPU. is doing the math from priv key to pub key. |
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hi,
for the first private key there are 2 bitcoin addresses the compressed one and the uncompressed one so there would really be 2^160/2 bitcoins addresses
Actually it would be 2^256/2 compressed 2^128 and uncompressed 2^128 since you're talking about the private key. As for the address it would be 2^160/2 compressed 2^80 and uncompressed 2^80. |
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Anybody thought about making VanitySearch search with a public key to decrease the search range to 2^128 like how kangaroo is but looking for prefixes?
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Sure. Load these 1000 example hex private keys (they are randomly generated which means don't waste your time trying to find money in these - I didn't load them). https://pastebin.com/WNJLJd2r (too large to post here) The script will analyze how frequently a 1 or 0 occurs in a certain bit position. Then it uses the analysis results to estimate the probability of a 1 or 0 occurring in that position. It also supports analyzing the probability of a sequence of bits occurring in multiple positions, but be warned that that increasing the number of positions to estimate together, uses a lot of memory (A LOT!). This particular output makes the following result: ========== TOP 260 MOST LIKELY BIT CONFIGURATIONS (Highest probability first) ==========
Output format: <probability> [(<bit number between 0-255>, <0 or 1>), ...]
[(9.399999999999997, [(33, 0)]),
(8.600000000000009, [(127, 1)]),
(7.399999999999995, [(135, 0)]),
(7.199999999999996, [(153, 0)]),
(6.999999999999995, [(53, 0)]),
(6.600000000000006, [(142, 1)]),
(6.400000000000006, [(32, 1)]),
(6.400000000000006, [(88, 1)]),
(6.400000000000006, [(186, 1)]),
(6.399999999999995, [(124, 0)]),
(6.399999999999995, [(227, 0)]),
(6.2000000000000055, [(43, 0)]),
(5.800000000000005, [(0, 0)]),
(5.800000000000005, [(2, 0)]),
(5.800000000000005, [(9, 0)]),
(5.600000000000005, [(76, 0)]),
(5.600000000000005, [(94, 1)]),
(5.600000000000005, [(111, 0)]),
(5.600000000000005, [(137, 1)]),
(5.600000000000005, [(196, 1)]),
(5.600000000000005, [(208, 1)]),
(5.400000000000005, [(16, 1)]),
(5.400000000000005, [(247, 1)]),
(5.200000000000005, [(15, 1)]),
(5.200000000000005, [(89, 1)]),
(5.200000000000005, [(147, 1)]),
(5.200000000000005, [(170, 1)]),
(5.200000000000005, [(190, 0)]),
(5.200000000000005, [(212, 0)]),
(5.200000000000005, [(219, 0)]),
(5.000000000000004, [(3, 1)]),
(5.000000000000004, [(36, 1)]),
(5.000000000000004, [(41, 1)]),
(4.800000000000004, [(165, 1)]),
(4.800000000000004, [(169, 0)]),
(4.600000000000004, [(19, 1)]),
(4.600000000000004, [(31, 1)]),
(4.600000000000004, [(112, 1)]),
(4.600000000000004, [(140, 1)]),
(4.600000000000004, [(191, 0)]),
(4.400000000000004, [(22, 1)]),
(4.400000000000004, [(93, 1)]),
(4.200000000000004, [(18, 0)]),
(4.200000000000004, [(64, 1)]),
(4.200000000000004, [(68, 0)]),
(4.200000000000004, [(84, 0)]),
(4.200000000000004, [(174, 1)]),
(4.200000000000004, [(181, 0)]),
(4.0000000000000036, [(117, 0)]),
(4.0000000000000036, [(144, 0)]),
(4.0000000000000036, [(195, 0)]),
(4.0000000000000036, [(199, 0)]),
(4.0000000000000036, [(213, 0)]),
(4.0000000000000036, [(228, 1)]),
(3.8000000000000034, [(26, 0)]),
(3.8000000000000034, [(47, 1)]),
(3.8000000000000034, [(77, 0)]),
(3.8000000000000034, [(109, 1)]),
(3.8000000000000034, [(168, 0)]),
(3.8000000000000034, [(222, 0)]),
(3.600000000000003, [(35, 0)]),
(3.600000000000003, [(61, 1)]),
(3.600000000000003, [(92, 0)]),
(3.600000000000003, [(224, 0)]),
(3.600000000000003, [(225, 1)]),
(3.600000000000003, [(232, 0)]),
(3.400000000000003, [(1, 0)]),
(3.400000000000003, [(40, 0)]),
(3.400000000000003, [(102, 1)]),
(3.400000000000003, [(116, 1)]),
(3.400000000000003, [(188, 1)]),
(3.400000000000003, [(244, 1)]),
(3.200000000000003, [(103, 1)]),
(3.200000000000003, [(108, 0)]),
(3.200000000000003, [(129, 0)]),
(3.200000000000003, [(132, 0)]),
(3.200000000000003, [(139, 1)]),
(3.200000000000003, [(159, 1)]),
(3.200000000000003, [(238, 0)]),
(3.0000000000000027, [(7, 0)]),
(3.0000000000000027, [(10, 1)]),
(3.0000000000000027, [(25, 1)]),
(3.0000000000000027, [(45, 1)]),
(3.0000000000000027, [(72, 1)]),
(3.0000000000000027, [(85, 0)]),
(3.0000000000000027, [(98, 1)]),
(3.0000000000000027, [(99, 1)]),
(3.0000000000000027, [(157, 0)]),
(3.0000000000000027, [(180, 1)]),
(3.0000000000000027, [(185, 0)]),
(3.0000000000000027, [(206, 1)]),
(3.0000000000000027, [(242, 0)]),
(2.8000000000000025, [(23, 0)]),
(2.8000000000000025, [(82, 1)]),
(2.8000000000000025, [(160, 1)]),
(2.8000000000000025, [(173, 0)]),
(2.8000000000000025, [(175, 1)]),
(2.8000000000000025, [(204, 1)]),
(2.8000000000000025, [(210, 0)]),
(2.8000000000000025, [(216, 0)]),
(2.8000000000000025, [(250, 0)]),
(2.6000000000000023, [(63, 1)]),
(2.6000000000000023, [(83, 0)]),
(2.6000000000000023, [(97, 0)]),
(2.6000000000000023, [(107, 0)]),
(2.6000000000000023, [(164, 0)]),
(2.6000000000000023, [(233, 0)]),
(2.6000000000000023, [(235, 0)]),
(2.6000000000000023, [(237, 0)]),
(2.400000000000002, [(105, 1)]),
(2.400000000000002, [(128, 0)]),
(2.400000000000002, [(149, 1)]),
(2.400000000000002, [(158, 0)]),
(2.400000000000002, [(162, 1)]),
(2.400000000000002, [(177, 0)]),
(2.400000000000002, [(179, 1)]),
(2.400000000000002, [(245, 0)]),
(2.200000000000002, [(14, 1)]),
(2.200000000000002, [(27, 1)]),
(2.200000000000002, [(34, 0)]),
(2.200000000000002, [(46, 0)]),
(2.200000000000002, [(95, 1)]),
(2.200000000000002, [(115, 0)]),
(2.200000000000002, [(134, 0)]),
(2.200000000000002, [(146, 1)]),
(2.200000000000002, [(183, 0)]),
(2.200000000000002, [(189, 1)]),
(2.200000000000002, [(200, 0)]),
(2.200000000000002, [(205, 0)]),
(2.200000000000002, [(241, 1)]),
(2.200000000000002, [(243, 1)]),
(2.0000000000000018, [(54, 0)]),
(2.0000000000000018, [(58, 0)]),
(2.0000000000000018, [(113, 0)]),
(2.0000000000000018, [(119, 1)]),
(2.0000000000000018, [(133, 0)]),
(2.0000000000000018, [(155, 1)]),
(2.0000000000000018, [(194, 0)]),
(2.0000000000000018, [(240, 1)]),
(1.8000000000000016, [(42, 0)]),
(1.8000000000000016, [(62, 0)]),
(1.8000000000000016, [(65, 1)]),
(1.8000000000000016, [(67, 0)]),
(1.8000000000000016, [(104, 0)]),
(1.8000000000000016, [(131, 0)]),
(1.8000000000000016, [(152, 1)]),
(1.8000000000000016, [(176, 0)]),
(1.8000000000000016, [(217, 0)]),
(1.8000000000000016, [(226, 1)]),
(1.6000000000000014, [(39, 0)]),
(1.6000000000000014, [(57, 0)]),
(1.6000000000000014, [(74, 1)]),
(1.6000000000000014, [(80, 1)]),
(1.6000000000000014, [(121, 1)]),
(1.6000000000000014, [(182, 0)]),
(1.6000000000000014, [(215, 1)]),
(1.4000000000000012, [(6, 1)]),
(1.4000000000000012, [(52, 0)]),
(1.4000000000000012, [(75, 1)]),
(1.4000000000000012, [(81, 1)]),
(1.4000000000000012, [(87, 1)]),
(1.4000000000000012, [(101, 1)]),
(1.4000000000000012, [(123, 1)]),
(1.4000000000000012, [(198, 1)]),
(1.4000000000000012, [(214, 0)]),
(1.4000000000000012, [(236, 1)]),
(1.4000000000000012, [(249, 1)]),
(1.4000000000000012, [(253, 0)]),
(1.4000000000000012, [(255, 0)]),
(1.200000000000001, [(66, 1)]),
(1.200000000000001, [(78, 1)]),
(1.200000000000001, [(86, 1)]),
(1.200000000000001, [(118, 1)]),
(1.200000000000001, [(120, 1)]),
(1.200000000000001, [(130, 0)]),
(1.200000000000001, [(136, 1)]),
(1.200000000000001, [(143, 0)]),
(1.200000000000001, [(145, 0)]),
(1.200000000000001, [(148, 0)]),
(1.200000000000001, [(156, 0)]),
(1.200000000000001, [(218, 1)]),
(1.200000000000001, [(220, 1)]),
(1.0000000000000009, [(4, 1)]),
(1.0000000000000009, [(12, 1)]),
(1.0000000000000009, [(37, 0)]),
(1.0000000000000009, [(71, 0)]),
(1.0000000000000009, [(79, 0)]),
(1.0000000000000009, [(96, 0)]),
(1.0000000000000009, [(141, 1)]),
(1.0000000000000009, [(172, 0)]),
(1.0000000000000009, [(192, 0)]),
(1.0000000000000009, [(202, 0)]),
(1.0000000000000009, [(209, 1)]),
(1.0000000000000009, [(211, 0)]),
(1.0000000000000009, [(254, 1)]),
(0.8084000000000005, [(33, 0), (127, 1)]),
(0.8000000000000007, [(8, 1)]),
(0.8000000000000007, [(21, 1)]),
(0.8000000000000007, [(56, 0)]),
(0.8000000000000007, [(69, 1)]),
(0.8000000000000007, [(106, 1)]),
(0.8000000000000007, [(122, 0)]),
(0.8000000000000007, [(150, 0)]),
(0.8000000000000007, [(163, 1)]),
(0.8000000000000007, [(167, 0)]),
(0.8000000000000007, [(193, 1)]),
(0.8000000000000007, [(203, 1)]),
(0.8000000000000007, [(207, 0)]),
(0.8000000000000007, [(229, 1)]),
(0.8000000000000007, [(230, 0)]),
(0.8000000000000007, [(248, 0)]),
(0.8000000000000007, [(252, 0)]),
(0.6955999999999993, [(33, 0), (135, 0)]),
(0.6767999999999994, [(33, 0), (153, 0)]),
(0.6579999999999994, [(33, 0), (53, 0)]),
(0.6364000000000002, [(127, 1), (135, 0)]),
(0.6204000000000004, [(33, 0), (142, 1)]),
(0.6192000000000001, [(127, 1), (153, 0)]),
(0.6020000000000001, [(127, 1), (53, 0)]),
(0.6016000000000004, [(33, 0), (32, 1)]),
(0.6016000000000004, [(33, 0), (88, 1)]),
(0.6016000000000004, [(33, 0), (186, 1)]),
(0.6015999999999994, [(33, 0), (124, 0)]),
(0.6015999999999994, [(33, 0), (227, 0)]),
(0.6000000000000005, [(20, 0)]),
(0.6000000000000005, [(29, 0)]),
(0.6000000000000005, [(48, 1)]),
(0.6000000000000005, [(49, 0)]),
(0.6000000000000005, [(50, 1)]),
(0.6000000000000005, [(90, 1)]),
(0.6000000000000005, [(114, 0)]),
(0.6000000000000005, [(151, 1)]),
(0.6000000000000005, [(166, 1)]),
(0.6000000000000005, [(171, 0)]),
(0.6000000000000005, [(197, 0)]),
(0.6000000000000005, [(221, 1)]),
(0.6000000000000005, [(234, 1)]),
(0.5828000000000003, [(33, 0), (43, 0)]),
(0.567600000000001, [(127, 1), (142, 1)]),
(0.550400000000001, [(127, 1), (32, 1)]),
(0.550400000000001, [(127, 1), (88, 1)]),
(0.550400000000001, [(127, 1), (186, 1)]),
(0.5504, [(127, 1), (124, 0)]),
(0.5504, [(127, 1), (227, 0)]),
(0.5452000000000002, [(33, 0), (0, 0)]),
(0.5452000000000002, [(33, 0), (2, 0)]),
(0.5452000000000002, [(33, 0), (9, 0)]),
(0.5332000000000009, [(127, 1), (43, 0)]),
(0.5327999999999994, [(135, 0), (153, 0)]),
(0.5264000000000003, [(33, 0), (76, 0)]),
(0.5264000000000003, [(33, 0), (94, 1)]),
(0.5264000000000003, [(33, 0), (111, 0)]),
(0.5264000000000003, [(33, 0), (137, 1)]),
(0.5264000000000003, [(33, 0), (196, 1)]),
(0.5264000000000003, [(33, 0), (208, 1)]),
(0.5179999999999993, [(135, 0), (53, 0)]),
(0.5076000000000003, [(33, 0), (16, 1)]),
(0.5076000000000003, [(33, 0), (247, 1)]),
(0.5039999999999993, [(153, 0), (53, 0)]),
(0.4988000000000009, [(127, 1), (0, 0)])]
The values on the left, are probabilities of the bit positions on the left (beginning with 0 and ending at 255) being a 1 or 0 respectively (the second number in the tuple). Normally when e.g. 40% of bits are 1, that means 60% of them are 0 (another version of this script using this algo can be found here https://gist.github.com/ZenulAbidin/63d13b05f5adda18f03ef2cab577fca3) But I am calculating the probabilities above, a little differently here. say 400 out of 1000 of keys have a specific bit at 1 like the example above, that would mean coefficient of 0.4 (where valid values are from 0 to 1 - likeliness of a bit being 1). 0.5 means that the bits are perfectly distributed evenly. So I take this coefficient and subtract it by 0.5 - which gives a metric of how "chaotic" this bit is [n.b. this has nothing to do with quantum computers and all that]. Negative values here means the bit is more likely to be zero, Positive means more likely to be one. But the larger the absolute value of this result, the less "chaotic" (less likely to vary among larger sample size) this bit is. This script multiplies these values by 2 to get a result between -1 and 1 (as opposed to -0.5 and 0.5), to allow interpretation as a percentage. So 0% would be absolute chaos (equivalent to 50/50 probability of 0 or 1 for a given position). 100% would be no chaos at all (since the bit stays the same) and this is what the script is measuring. <so this script in the form I showed you does not help you guess private key bits, it's just a scientific study of random numbers.>
The other script I linked in this post is using regular probabilities, not chaos percentages by the way. I tested it and tried it, I kinda wish you could do that with the addresses. |
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Update: I now have a way to empirically guess some ofthe bits of private keys that are randomly generated: https://gist.github.com/ZenulAbidin/ac00845048c36ceb81df6bc47cbcb4e5# First, run this in shell:
# j=1000 # Sample size, feel free to change.
# for ((i=0; $i<$j; i++)); do openssl rand -hex 32 >> ~/Documents/randalysis.txt; echo -en "\n" >> ~/Documents/randalysis.txt; done
# sed -i '/^\s*$/d' ~/Documents/randalysis.txt
# pip install bitarray
import bitarray
import bitarray.util
from os.path import expanduser
sample=1000 # This is the number of example private keys you have generated above.
nkeys = 260 # This is the number of keys you want, change it accordingly.
home = expanduser("~")
with open(home+"/Documents/randalysis.txt", "r") as fp:
s = fp.read()
l = s.split("\n")
l = l[:-1] # Remove extraneous blank element
cl = [0]*256
delta = [0]*256
# Values closer to zero - more likely to be zero, values closer to one - more likely to be one
for i in l:
ba = bitarray.util.hex2ba(i)
ba.reverse()
for j in range(0, 256):
cl[j] += ba[j]
delta[j] = cl[j]/sample
# Values negative - more likely to be zero, values positive - more likely to be one
# Because values will be between -0.5 and 0.5, we must "explode" this
# (by two to get values from -1 to 1)
# to get percentages between 0% and 100% for likely zero and likely one.
deltat = [((delta[t] - 0.5)*2, t) for t in range(0,256)]
deltat = sorted(deltat, key=lambda x: abs(x[0]), reverse=True)
#deltat.sort(key=lambda x: x[0])
#deltatmin = [t for t in deltat if t[0] < 0.5]
#deltatmax = [t for t in deltat if t[0] > 0.5]
#deltatmax.sort(key=lambda x: x[0], reverse=True)
#deltateq = [t for t in deltat if t[0] == 0.5]
#delta = [d - 0.5 for d in delta]
import itertools
lz = []
# Adjust this value to return the combinations of probabilities for larger groups of bits.
# Warning: you should experiment with this setting, because setting this too high will
# make so many combinations, it will finish your RAM.
# Probability theory means that the probability of multiple bits having some specific values is
# __MUCH LESS__ than the probability of one bit having a aprticular value.
maxcomb = 2
for i in range(1,maxcomb+1):
cdt = itertools.combinations(deltat, i)
for c in cdt:
total_prob = 1
bit_configs = []
for it in c:
if it[0] < 0:
# Zero
bit_configs.append((it[1], 0))
elif it[0] < 1:
# One
bit_configs.append((it[1], 1))
else:
# perfectly even, no bias here so skip to next one
continue
total_prob *= abs(it[0])
lz.append((total_prob*100, bit_configs))
lz.sort(key = lambda x: x[0], reverse=True)
import pprint
print("========== TOP {} MOST LIKELY BIT CONFIGURATIONS (Highest probability first) ==========".format(nkeys))
print("Output format: <probability> [(<bit number between 0-255>, <0 or 1>), ...]")
pprint.pprint(lz[0:nkeys]) The probabilities are quite small though (as they should be), and the highest probabilities are still less than 10%. This could indicate some bias in OpenSSL RNG depending on what the user is doing on the mouse and keyboard... Can you provide an example |
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Github page: https://github.com/albertobsd/keysubtracterI publish this code on github in April 5, but i never update it until now, this is a small tool to make some substracted publickeys, address or hashes rmd160 from a target publickey. This can be useful to increment our chances of hit some privatekey of the puzzles. For example to make 100 copies of the puzzle 120 you need to execute the next command: ./keysubtracter -p 02ceb6cbbcdbdf5ef7150682150f4ce2c6f4807b349827dcdbdd1f2efa885a2630 -n 100 -b 120 Output 03f1d41da8acf0506f3bf7140b5629dd33a5cf546133479530cd8065e335a97666 # - 13292279957849158729038070602803446
022ec3a210bcb8ef6cf7b703b39539a83dc0c1318ccdb42daf48db2f0742971239 # + 13292279957849158729038070602803446
02b70ae2dcb442548570313f652b91ca093a3acac3a2441cb64614e8195505b6b8 # - 26584559915698317458076141205606892
0367dabeef20a6a8b7b5555b162cc8c8489e3134dcec624fe028573c34dbbf20f6 # + 26584559915698317458076141205606892
02a1d21298779f888cd8169f9ed59e4383219cdadbdb342ba886034ef9013b89be # - 39876839873547476187114211808410338
02ae015703cbaee9570dc648d7bce78ac7cb438630e09d69eef4f1208655e1027d # + 39876839873547476187114211808410338
(Output omitted)
but what that mean: ./keysubtracter -p <publickey to be substracted> -n <number of requested keys> -b <bit range> Output: New Publickey 1 # offset from original publickey
New Publickey 2 # offset from original publickey
...
How many bits does it subtract it by? |
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today i successfully jump from 110 bit back toward target key range but i landed 5 bit down the range of target key , still figuring it out lets see ~~ math is beautiful and hell big  in range  How long did it take for you to do that? |
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if the above message was for me,than no, i didn't find it. over 600k addresses which are quite good (i can make statistics to see where the prefix was most common/uncommon). I know that there is no direct relationship between address and "key space" but it's good to analyze. i modified now the search and the estimate is ~ 25-30 days with a success rate of 2%. i was quite thrilled when i saw the program found the following address "16jY7qLzk131JDX93LZSSfQQZAFjtthXQN" not many addresses in the 64 range that starts with 16jY7q and ends with XQN How would I calculate how many combinations of 16jY7q would be in 2^64 range?
You can't, because there is no direct relation between an address/pubkeyhash and its private key. It's the fact that the address is a hashed (and encoded) public key that makes it impossible to probe/guess the range of its private key. This also applies even if you have the end/middle of an address instead of the beginning of it. |
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I have generated almost 300.000 addresses starting with 16jY7q and still the 64 puzzle isn't among them. Maybe soon i will find it  The progress right now is over 70%... 5-6 days left to reach 100%. Ofcourse is not full keyspace search (dont have that many resources) but an improved search algorithm. #64
from
0000000000000000000000000000000000000000000000008000000000000000
to
0000000000000000000000000000000000000000000000010000000000000000
16jY7qL111111111111111111111111111 =hash160> 003EE4133D831A64D9DB3482984C1EDBBDBCE4B21678000000
16jY7qLzzzzzzzzzzzzzzzzzzzzzzzzzzz =hash160> 003EE4133DCAE1C772B44626BE1DE2197F8D2F780B9FFFFFFF
00FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
003EE4133DCAE1C772B44626BE1DE2197F8D2F780B9FFFFFFF - 003EE4133D831A64D9DB3482984C1EDBBDBCE4B21678000000 = 0x47C76298D911A425D1C33DC1D04AC5F527FFFFFF
00FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF / 0x47C76298D911A425D1C33DC1D04AC5F527FFFFFF = 0x39106D151
0x8000000000000000 / 0x39106D151 = 0x23E3B14C
Probability theory hash160 in #64 range You will find N counts private key and Compressed Address is 16jY7qL****************** 7 Character match N = 0x23E3B14C (HEX) N = 602,124,620 (DEC) I wonder what happened its almost a month later, did you ever find it? |
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He who owns the private key, owns the cryptocurrency and that's a fact.
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In that case this should do the trick: EDIT NUMBER 3: THIS VERSION ACTUALLY WORKS USE THIS ONE from fastecdsa import curve
from fastecdsa.point import Point
import bit
G = curve.secp256k1.G
N = curve.secp256k1.q
def pub2point(pub_hex):
x = int(pub_hex[2:66], 16)
if len(pub_hex) < 70:
y = bit.format.x_to_y(x, int(pub_hex[:2], 16) % 2)
else:
y = int(pub_hex[66:], 16)
return Point(x, y, curve=curve.secp256k1)
# This function makes all the downscaled pubkeys obtained from subtracting
# numbers between 0 and divisor, before dividing the pubkeys by divisor.
def shiftdown(pubkey, divisor, file, convert=True):
Q = pub2point(pubkey) if convert else pubkey
print(Q, 'QQ')
# k = 1/divisor
k = pow(divisor, N - 2, N)
for i in range(divisor+1):
P = Q - (i * G)
P = k * P
if (P.y % 2 == 0):
prefix = "02"
else:
prefix = "03"
hx = hex(P.x)[2:].zfill(64)
hy = hex(P.y)[2:].zfill(64)
file.write(prefix+hx+"\n") # Writes compressed key to file
factor = 32
with open("input.txt", "r") as f, open("output.txt", "w") as outf:
line = f.readline().strip()
while line != '':
shiftdown(line, factor, outf)
line = f.readline().strip()
This is for all keys in one file, I technically *could* script the case of one set of shifted keys per file, but then it requires an argc/argv switch to toggle the one you want and implementing that will bloat the code size 
EDIT: I had posted an older version of the script which people complained had a bunch of errors, admittingly I did not test this version with the file input since the base script was already "bug free" I thought these should be straightforward changes... well now I know  After some proper testing, I got rid of a bunch of artifacts from older script versions that were triggering lint errors, and the result is posted here, above. What are the requirements for using this, I'm using python 3 on windows and still getting errors. |
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