Preserving Privacy in Ethereum Blockchain

Data transparency is one of the prime essence of Ethereum, because of which users cannot fake the transactions, a similar strategy that even Bitcoin follows. However, this very nature of Ethereum has made the blockchain vulnerable to security threats and attacks, this is due to the fact that transparency comes in a trade off with privacy. Lack of sophisticated privacy preservation techniques in Ethereum might eventually pave a way to attacks on the users and the blockchain itself. In this paper, we use the Ethereum blockchain transaction data of the January-2019 from Etherscan, constructed a graph/network and extracted informations using network measures like degree centrality, betweenness centrality, Eigen centrality, Page rank centrality, Minimum spanning tree and it’s associated node degrees, and prove that application of these kinds of network measures breaches the privacy of the Ethereum transactions (blockchains) while putting few active participants of the Ethereum blockchain under the risk of attack. In this context, two algorithms are proposed, one based on the chaotic maps and the other on the differential privacy to encrypt the edge weights of the transaction network which in turn leads to addition of the noise into the data set before release to the public. In specific, noise is added to the transacted amount information. A third party without the knowledge of the algorithm gets a false information due to the noisy data set. The whole process of privacy preservation is looked after by set of dedicated distributed servers which includes adding noise for privacy preservation and retrieving the original data back from noisy version when authenticated requests are made.