Create README.md

README.md

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+# Pollard's kangaroo for SECPK1
+
+A simple Pollard's kangaroo solver for SECPK1
+
+Structure of the input file:
+* All values are in hex format
+* Public keys can be given either in compressed or uncompressed format
+
+```
+Start range
+End range
+Key #1
+Key #2
+...
+```
+
+ex
+
+```
+49dccfd96dc5df56487436f5a1b18c4f5d34f65ddb48cb5e0000000000000000
+49dccfd96dc5df56487436f5a1b18c4f5d34f65ddb48cb5effffffffffffffff
+0459A3BFDAD718C9D3FAC7C187F1139F0815AC5D923910D516E186AFDA28B221DC994327554CED887AAE5D211A2407CDD025CFC3779ECB9C9D7F2F1A1DDF3E9FF8
+0335BB25364370D4DD14A9FC2B406D398C4B53C85BE58FCC7297BD34004602EBEC
+```
+
+# How it works
+
+The program uses 2 herds of kangaroos, a tame herd and a wild herd. When 2 kangoroo (a wild one and a tame one) collide, the key
+can be solved. Due to the birtday paradox, a collision happens (in average) after 2*sqrt(k2-k1) iterations, the 2 herds have the 
+same size. Here is a brief description of the algoritm:
+
+We have to solve P = k.G, we know that k lies in the range ]k1,k2], G is the SecpK1 generator point.
+
+n = floor(sqrt(k2-k1))+1
+
+* Create a jump table point jP = [G,2G,4G,8G,...2^nG], 
+* Create a jump distance table jD = [1,2,4,8,....2^n]
+
+tame<sub>i</sub> = rand(0..k2-k1)</br>
+tamePos<sub>i</sub> = tame<sub>i</sub>.G</br>
+wild<sub>i</sub> = rand(0..k2-k1)</br>
+wildPos<sub>i</sub> = P + wild<sub>i</sub>.G</br>
+
+while not collision between tamePos<sub>i</sub> and wildPos<sub>i</sub> {</br>
+&nbsp;&nbsp; tamePos<sub>i</sub> = tamePos<sub>i</sub> + jP[tamePos<sub>i</sub>.x % n]</br>
+&nbsp;&nbsp;  tame<sub>i</sub> += jD[tamePos<sub>i</sub>.x % n]</br>
+&nbsp;&nbsp;  wildPos<sub>i</sub> = wildPos<sub>i</sub> + jP[wildPos<sub>i</sub>.x % n]</br>
+&nbsp;&nbsp;  wild<sub>i</sub> += jD[wildPos<sub>i</sub>.x % n]</br>
+}</br>
+
+(t,w) = index of collision</br>
+K = k1 + tame<sub>t</sub> - wild<sub>w</sub></br>
+
+
+