Homomorphic encryption (HE)
What is homomorphic encryption?
HE allows marketing analysts to perform computations on encrypted data without having to decrypt it first, which is especially suitable for businesses that handle very sensitive data in zero-trust environments.
For example, given the ensuing medical data privacy concerns, predictive analytics in healthcare can be hard to apply via a 3rd party provider, unless operating on encrypted data.
What are the benefits of homomorphic encryption?
Performing calculations on encrypted data allows marketing data processing to be outsourced to a 3rd party vendor with very little care, for the simple reason that without the proper decryption key — the original data cannot be accessed.
The ability to perform encrypted data processing can solve a variety of major business challenges faced by cross-industry marketers, which is why HE can be applied to a wide spectrum of use cases, including private data analytics.
What are the limitations of homomorphic encryption?
In theory, everyone should be using homomorphic encryption. So why isn’t it more widely used?
The problem with full HE is that it isn’t efficient — yet. Meeting the requirements of full homomorphism comes with extremely slow algorithms that can have very high storage requirements. So, while it may not be a very viable option today, HE might just become a commonly used solution in the near future.
Key takeaways
- Homomorphic encryption allows analysts to perform computations on encrypted data without having to decrypt it first.
- It can be used for privacy-preserving outsourced storage and computation, and is especially suitable for businesses that handle very sensitive data in zero-trust environments.
- While HE is not widely used in business as of yet, because of its very slow performance and heavy storage demands, ever evolving technological advancements could turn it in the near future into an integral component of any business operating in data-sensitive arenas.
