Calculations to compute the expected Bitcoin transaction efficiency (measured in transactions per block) using CRYSTALS-Dilithium vs using Elliptic Curve Digital Signature Algorithm (ECDSA).
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Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multi-conditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3\% more one-time change addresses than other heuristics.
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The dataset contains development archives of more or less interesting conversations, announcements, discussions, presentations and so on regarding consensus changes in Bitcoin.
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WannaCry Bitcoin Cash-in and Cash-out payment network data in JSON along with STIX representation of address 12t9YDPgwueZ9NyMgw519p7AA8isjr6SMw12t9YDPgwueZ9NyMgw519p7AA8isjr6SMw
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