Homomorphic Encryption & Secure Multiparty Computation
How to Use Data Without Seeing It
About
Privacy is a big concern when handling sensitive data. Many security issues happen because someone gets access to data they shouldn’t see — whether by accident or on purpose. But what if we could eliminate this risk entirely? What if we could process data without anyone actually seeing it?
This is exactly what homomorphic encryption (HE) and secure multiparty Computation (SMPC) do. These advanced techniques allow people to work with data while keeping it completely private.
Homomorphic Encryption: Doing Math on Encrypted Data
Normally, when you want to analyze data — like calculating the average salary in a company — you first have to decrypt the data so you can use it. This creates a security risk because anyone with access to the data during the process could see sensitive information.
Homomorphic encryption (HE) solves this problem by allowing data to stay encrypted while being processed. It’s like a locked safe that still lets you do calculations on the contents without opening it.
For example, imagine a company wants to calculate the average employee salary while keeping individual salaries private. With HE:
The salaries stay encrypted— even while being used for the calculation.The final result is also encrypted— so no one can see the individual salaries along the way.Only the person with the "decryption key"can unlock the final result.
This means that even if a hacker, cloud provider, or external consultant tries to access the data during the calculation, all they see is meaningless encrypted numbers — nothing useful.
However, there’s a catch: HE is extremely slow because every step of the calculation has to be converted into a special encrypted format. For complex operations, this can take a long time, making HE impractical for some real-world applications.
Secure Multiparty Computation: Splitting Data for Secure Collaboration
Now, let’s say different people or companies want to work together on a joint analysis without revealing their private data to each other. Secure multiparty computation (SMPC) makes this possible by breaking data into secret pieces and distributing them among participants.
Imagine three companies want to calculate the average employee salary across all their employees, but they don’t want to share their actual payroll data with each other. Here’s how SMPC would work:
Each company
splits their salary data into small, meaningless fragments.These fragments are shared among the participants in such a way that
no single party can reconstruct the original data.When combined correctly, the system can compute the
final result(the average salary)without anyone ever seeing the full dataset.
The key advantage of SMPC is that it allows multiple parties to work together without sharing sensitive data. However, for SMPC to work, all participants need to follow the rules. If one or more parties try to cheat (or if they experience a system failure), the entire process can break down.
Are These Methods Practical?
Both HE and SMPC provide strong security guarantees, but they have limitations:
HE is computationally expensive— it’s slow and can’t handle all types of calculations efficiently.SMPC requires trust between multiple participants— if some parties act dishonestly or experience outages, the process fails.
Despite these challenges, both methods are already being used in real-world applications:
HE is useful in finance— allowing banks to analyze encrypted customer data without ever accessing personal details.SMPC is used in healthcare— where hospitals can collaborate on research without exposing patient data.
These technologies are changing the way companies protect privacy, making it possible to gain valuable insights from data while keeping it completely secure.