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Enhancing privacy and scalability of permissioned blockchain

Enhancing privacy and scalability of permissioned blockchain
Enhancing privacy and scalability of permissioned blockchain
Blockchain is an emerging technology that offers fascinating properties of data integrity and non-repudiation. As the name suggests, it consists of consecutive chained blocks, replicated on all network nodes, containing asset transactions. Blocks are linked together via hashing procedures and created in a distributed fashion with consensus protocols. Besides, blockchain provides smart contracts, self-executable programs that allow to realise fully decentralised and tamper-proof applications. In this thesis, we analyse five prominent blockchain platforms, i.e. Bitcoin, Ethereum 2.0, Algorand, Ethereum-private and Hyperledger Fabric. We evaluate their security according to the used consensus algorithm, the overall infrastructure and the smart contracts vulnerabilities. We then focus on permissioned blockchains, operated by authenticated parties. Although they can enforce access control rules and are more efficient compared to permissionless, they still lack of data privacy and present scalability issues. A typical privacy solution is to encrypt data before being stored on blockchain, but the downside is that smart contracts can no longer execute functions on them. We thus propose to combine blockchain with Homomorphic Encryption (HE), a cryptographic model that allows to perform computations on ciphertexts. We show how blockchain coupled with HE can be beneficially applied to Smart Grid to realise privacy-preserving energy billing and trading. HE has a limitation, however: it precludes computations on data encrypted with different keys. To overcome this, we extend the previous integration with Multi-Key HE (MKHE). We thus present PANTHER, a MKHE-integrated permissioned blockchain, where users can run smart contracts over ciphertexts created with different keys. Results of MKHE computations are then decrypted in PANTHER via secure multiparty protocols among users. For scalability instead, blockchain exhibits an intrinsic problem: adding new nodes to cope with increasing demands worsens performance, due to a lengthening of the time to reach consensus. We thus present SHERLOCK, a permissioned blockchain in which consensus nodes are split into committees and disposed on a two-layer ring-based architecture. SHERLOCK uses the sharding technique to assign incoming transactions to committees, which process them in parallel, thereby boosting performance.
blockchain, data privacy, scalability, homomorphic encryption, secure multiparty computation, sharding
University of Southampton
Zanfino, Gilberto
5d1051e5-b461-4d7c-89cb-2fd071c7446a
Zanfino, Gilberto
5d1051e5-b461-4d7c-89cb-2fd071c7446a
Sassone, vladi
df7d3c83-2aa0-4571-be94-9473b07b03e7
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3

Zanfino, Gilberto (2024) Enhancing privacy and scalability of permissioned blockchain. University of Southampton, Doctoral Thesis, 169pp.

Record type: Thesis (Doctoral)

Abstract

Blockchain is an emerging technology that offers fascinating properties of data integrity and non-repudiation. As the name suggests, it consists of consecutive chained blocks, replicated on all network nodes, containing asset transactions. Blocks are linked together via hashing procedures and created in a distributed fashion with consensus protocols. Besides, blockchain provides smart contracts, self-executable programs that allow to realise fully decentralised and tamper-proof applications. In this thesis, we analyse five prominent blockchain platforms, i.e. Bitcoin, Ethereum 2.0, Algorand, Ethereum-private and Hyperledger Fabric. We evaluate their security according to the used consensus algorithm, the overall infrastructure and the smart contracts vulnerabilities. We then focus on permissioned blockchains, operated by authenticated parties. Although they can enforce access control rules and are more efficient compared to permissionless, they still lack of data privacy and present scalability issues. A typical privacy solution is to encrypt data before being stored on blockchain, but the downside is that smart contracts can no longer execute functions on them. We thus propose to combine blockchain with Homomorphic Encryption (HE), a cryptographic model that allows to perform computations on ciphertexts. We show how blockchain coupled with HE can be beneficially applied to Smart Grid to realise privacy-preserving energy billing and trading. HE has a limitation, however: it precludes computations on data encrypted with different keys. To overcome this, we extend the previous integration with Multi-Key HE (MKHE). We thus present PANTHER, a MKHE-integrated permissioned blockchain, where users can run smart contracts over ciphertexts created with different keys. Results of MKHE computations are then decrypted in PANTHER via secure multiparty protocols among users. For scalability instead, blockchain exhibits an intrinsic problem: adding new nodes to cope with increasing demands worsens performance, due to a lengthening of the time to reach consensus. We thus present SHERLOCK, a permissioned blockchain in which consensus nodes are split into committees and disposed on a two-layer ring-based architecture. SHERLOCK uses the sharding technique to assign incoming transactions to committees, which process them in parallel, thereby boosting performance.

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More information

Published date: June 2024
Keywords: blockchain, data privacy, scalability, homomorphic encryption, secure multiparty computation, sharding

Identifiers

Local EPrints ID: 491205
URI: http://eprints.soton.ac.uk/id/eprint/491205
PURE UUID: 1555cc2c-337c-4e0f-bd16-e20602bec2da
ORCID for Gilberto Zanfino: ORCID iD orcid.org/0000-0002-5576-3246
ORCID for vladi Sassone: ORCID iD orcid.org/0000-0002-6432-1482
ORCID for Leonardo Aniello: ORCID iD orcid.org/0000-0003-2886-8445

Catalogue record

Date deposited: 17 Jun 2024 16:43
Last modified: 21 Sep 2024 01:56

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Contributors

Author: Gilberto Zanfino ORCID iD
Thesis advisor: vladi Sassone ORCID iD
Thesis advisor: Leonardo Aniello ORCID iD

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