Introduction

Data encryption is one of the smartest things any organization can do to protect the privacy and security of confidential and sensitive data. Using a unique encryption key, data is converted to an intermediate representation known as “ciphertext,” which usually appears as a jumbled mixture of letters and numbers to the human eye. This encrypted data will be meaningless to anyone without the corresponding decryption key—even malicious actors who breach an organization’s defenses.

In recent years, we’ve seen the emergence of a new data encryption paradigm known as “homomorphic encryption.” So what is homomorphic encryption, exactly, and what are the use cases of homomorphic encryption? We have all the answers below.

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Table of Contents

What Is Homomorphic Encryption?

Imagine placing a book inside a box, which you then lock with a key. This protects the book from being accessed by unauthorized people, but it also requires you to unlock the box if you want to read the book or access any of its information.

Traditional data encryption has functioned in a similar way as our book/box analogy. The good news is that modern encryption algorithms are highly secure: once the data is encrypted, it’s considered protected (since cracking the encrypted data would require tremendously expensive amounts of processing power). The bad news is that the encrypted data is unusable by everyone—at least until it’s converted back to its original representation using the decryption key.

Homomorphic encryption is an alternate encryption technique in which users can perform computations on the encrypted data without having to decrypt it. In our analogy, homomorphic encryption would somehow allow you to read or write to the book, even while it’s still locked inside the box. The results of these computations remain encrypted, and users cannot view or understand the results without the necessary decryption key.

There are three main types of homomorphic encryption. These are classified based on the kind of mathematical operations that can be performed on the encrypted data:

  • Partially homomorphic encryption (PHE) only supports limited functionality (e.g., multiplication or addition, but not both). However, an operation can be performed an unlimited number of times.
  • Somewhat homomorphic encryption (SHE) supports a greater range of functionality, but operations can only be performed a limited number of times.
  • Fully homomorphic encryption (FHE) supports arbitrary operations and computations that can be performed an unlimited number of times.

FHE is the “gold standard” for homomorphic encryption techniques but is currently far too slow to be practical for many use cases. The first FHE scheme, which was proposed by Craig Gentry in 2009, was reportedly 100 trillion times slower than plaintext operations.

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Use Cases for Homomorphic Encryption

Given the impracticality of current FHE methods, researchers are investigating ways to speed it up and make it useful for real-world applications (see IBM’s HElib library or Microsoft SEAL). FHE remains an active area of interest for many tech giants. In June 2021, Google announced the release of an FHE "transpiler": a program that converts developers' code into an equivalent version that can work with encrypted data instead.

Below are just a few use cases for homomorphic encryption:

  • Search engine privacy: With homomorphic encryption, users could theoretically enter a query into Google or another website, and then get back the results, without the search engine knowing what it is they searched for. The query would be encrypted using homomorphic encryption, then processed by the server without decrypting it.
  • Improving cloud security: Working with sensitive and encrypted data in the cloud can be awkward and time-consuming. For example, you might need to download the encrypted data, perform the desired operations, and then re-encrypt it and upload it to the cloud again. Homomorphic encryption could allow you to perform computations in the cloud on the encrypted data, saving time and effort.
  • Election integrity: Homomorphic encryption could help protect the identities of voters while ensuring that elections are transparent and secure. To determine the winner of an election, each encrypted vote could be added up, preventing malicious third parties from manipulating the results.

How Xplenty Can Help with Data Security

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There are a tremendous number of possible use cases for homomorphic encryption—but as of writing, they’re little more than a pipe dream due to the computational complexity. What you need is a data encryption solution that works right now to keep sensitive and confidential information safe.

Xplenty is a robust, feature-rich ETL tool that makes security a priority. The Xplenty platform uses SSL/TLS encryption across the board to protect data in transit and at rest; we also follow best practices for physical, network, and system security. Plus, Xplenty’s drag-and-drop interface, and rich array of connectors and integrations, make it the most user-friendly choice for building automated data pipelines to your cloud data warehouse or data lake.

Related Reading: Xplenty Security

Want to learn more about how Xplenty can protect your sensitive enterprise data? Get in touch with our team of data experts today for a chat about your business goals and requirements, or to start your 14-day pilot of the Xplenty platform.