Multi-Party Computation, or MPC for short, is a cryptographic technique that allows multiple parties to jointly compute a function using their private inputs without revealing those inputs to each other.
In simpler terms, MPC is a way for different entities to collaborate and analyse data without actually sharing the data itself. This is achieved through sophisticated cryptographic protocols, including encryption, that ensure the privacy, security, and accuracy of the computation.
Developed through the contributions of various researchers over several decades, MPC relies on a process called input sharing. Each party involved in the collaboration shares a portion of their private data, which is then divided and distributed into pieces called "shares." These shares are used in the secure computation phase, where each party executes specific cryptographic operations on their share without revealing the underlying data. This process continues iteratively until the correct final result is obtained while maintaining the confidentiality of all individual data.
The final step involves output reconstruction, where the parties combine their individual results to obtain the final outcome of the computation.
MPC's emphasis on privacy and accuracy makes it valuable in real-world applications, particularly in sectors with strict data privacy regulations, such as finance and healthcare.
For instance, banks can use MPC to collaboratively analyse customer data for fraud detection or risk assessment without sharing any sensitive information. Similarly, medical researchers can leverage MPC to analyse patient data for disease patterns, treatment development, or clinical trials, all while adhering to patient confidentiality.
In today's digital age, where data privacy is paramount, Multi-Party Computation is emerging as a powerful tool for collaborative data analysis, ensuring that privacy and confidentiality remain at the forefront of data-driven decision-making.
