Secure Multi-Party Computation(SMPC) is a cryptographic method suitable to allow several parties to jointly compute a function of their private inputs without the need to reveal their private inputs to each other. It allows computing in the open without needing to divulge any private data.
How Does SMPC Work?
SMPC consists of a number of cryptographic protocols that allows parties to:
Sharing of input: Both parties encrypt their input data through a particular protocol.
The developers then assemble this (often, gigantic) family of computations in such a way that these functions cannot be inferred neither side of the communication besides obtaining the ultra transcended hash of the function and indentation aligned chain.
Share Output: The result of the computation, decrypted, is shared with all parties.
Applications of SMPC
Applications of SMPC include the following:
Privacy-Preserving Data Analysis: Analysis of sensitive data across organizations while avoiding sharing raw data.
Sealed bid Auctions: Run auctions in which bidders can open their bids but no other bidders see them
Private Set Intersection: Two or more private sets identify common elements among themselves without revealing elements of the sets.
Private Training of Machine Learning: Training machine learning models using private data held by different parties, without having to share the data.
Related terrain and future work
But despite its possibilities, SMPC faces some challenges:
SMPC protocols: The complexity in computation — especially for the complex computation.
Potentially Large Communication Overhead: Communicating between parties can incur great overhead, which can be debilitating for larger datasets.
Security has because the security of SMPC protocols depends on the assumption of strong cryptographic, there might be future attacks that will be capable of and how harsh it could be.
Following these problems, practitioners are researching towards finding run-time friendly SMPC protocols. With better technology, SMPC is going to be critical for secure collaborative data analysis in many fields.
