Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity
Keywords:
Secure Multi-Party Computation, Privacy-Preserving Analytics, CybersecurityAbstract
Secure Multi-Party Computation changes private cybersecurity data analytics. As sophisticated attackers and sensitive data evolve, cybersecurity calls for privacy-preserving analytics. Theoretical basis, implementation, and performance of SMPC privacy-preserving data analytics methods are investigated.
Many participants in SMPC cryptography can calculate a function over their secret inputs without disclosing them. Security calls for integrity and data privacy, therefore SMPC. We study homomorphic encryption, oblivious transmission, and safe function evaluation in SMPC. Background clarifies how multi-party safe computing is made possible by these systems.
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