Privacy-Preserving Models and Systems in Blockchain

Dear Colleagues,

Protecting personal data and ensuring privacy while allowing for data processing and analysis is a key ongoing challenge. While traditional information systems, centred on remote data servers, have shown limitations when it comes to protecting identifying data, blockchain technology—which connects decentralised personal devices—appears to hold great promise for building solutions that truly guarantee privacy. Privacy-preserving techniques allow us to minimise the amount of personal data that is collected and processed within the digital system; however, it must still be possible to identify people in the real world. The need to ensure data protection concerns both industry and society at large. The protection of industrial non-personal data is also a challenge because, when intercepted, exploitation of this data impedes the construction of a data-driven economy.

Blockchain technologies provide a PKI-less transparent system, meaning that no metadata, including personal identifiers, is used. Instead, a cryptographic credential used as an account address authenticates the asset owner. The transparence property of the ledger implies that no sensitive, business, or identifying data should be recorded, even ciphered, through a blockchain. This leads to the deployment of an additional off-chain network to store and securely exchange the data. Moreover, transparency enables traceability—and, through this, private information may be deduced. While learning or decentralised computing may require a large quantity of data and/or trace of data, how can we be sure that no private information can be deduced from the collected dataset?

Submissions may include original research, comprehensive reviews, case studies, frameworks, and experimental studies focusing on privacy-preserving models and systems that use blockchain technologies.

Dr. Christine Hennebert
Guest Editor

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• privacy-enhancing technologies (PETs) through blockchain
• cryptographic anonymous credentials
• secure multiparty computation
• advanced digital signatures, ring signatures, and blind signatures
• attribute-based credentials
• PETs within supply chains, medical domain, transportation, etc.
• usage of blockchain and IPFS
• privacy-enhancing information system architectures
• data security at storage and during transfer