Privacy-Preserving Computation and Web3

Jerry Huang (), Ken Huang () and Mudi Xu ()
Additional contact information
Jerry Huang: The University of Chicago
Ken Huang: DistributedApps LLC
Mudi Xu: Cloud Security Alliance

Chapter Chapter 10 in Web3 Applications Security and New Security Landscape, 2024, pp 209-236 from Springer

Abstract: Abstract This chapter examines techniques for Privacy-Preserving computation that can be applied in blockchain and Web3 systems. It analyzes privacy issues at the blockchain network layer and explores privacy enhancing protocols. Core techniques discussed include homomorphic encryption, secure multiparty computation, zero-knowledge proofs, and differential privacy. Integration strategies are provided such as performance optimization, modular development, open standards, and enhancing user experience. The outlook highlights future threats like quantum computing and scale, matched by ongoing innovation across cryptographic protocols, Generative AI integration, and community collaboration.

Keywords: Homomorphic encryption; Secure multiparty computation (MPC); Zero-knowledge proofs (ZKPs); Differential privacy (DP); Decentralized identifiers (DIDs); Privacy regulations and compliance; Generative AI (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-031-58002-4_10

Ordering information: This item can be ordered from
http://www.springer.com/9783031580024

DOI: 10.1007/978-3-031-58002-4_10

Access Statistics for this chapter

More chapters in Future of Business and Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().