The US Defense Department is looking at what could be considered the “ holy grail of data encryption, ” which would close a loophole that would allow hackers to access sensitive information while it is being processed.

If someone wanted to use the encrypted data to do something useful, he would first have to decrypt it into so-called “plain text”, making it prone to poking around again. To help protect that now-decrypted information, those who work with the plain text are usually only quickly trusted computers But as regular headlines about data breaches at large organizations show, it is becoming increasingly difficult to determine which devices are safe.

“Given all the news about these hacks, these malware attacks, we can’t fully trust all of our hardware or software systems,” Tom Rondeau, a program manager at the Defense Advanced Research Projects Agency (DARPA), told Live Science.

Related: Mind Control Flying Saucers: 22 Released Military Secrets

That’s why DARPA tries to make breakthroughs in something called completely homomorphic encoding (FHE). The technique makes it possible to analyze calculation data while it is still in coded form. That could allow researchers to search sensitive bank data without, for example, disclosing customer data, or having health researchers analyze personal health data while preserving patient privacy, Rondeau said. The technique can also help the military more secure their battlefield data and make it easier for allies to work with classified intelligence data.

The key to the approach is in the name, which is derived from the Latin words ‘homos’, meaning ‘the same’, and ‘morphe’, meaning ‘shape’. It refers to the fact that certain mathematical operations can map data from one form to another without changing the underlying structure of the data. This means that changes made to the data in one form are preserved when that data is converted back to the other. This principle can be applied to encryption because computers display all data, including text, as numbers.

Here’s a much simplified example of how this could work: Imagine an encryption scheme that encrypts data by multiplying it by 3, so if you encrypt the number 8, you get 24. If you multiply your encrypted data by 2, you get your 48. If you encrypt the number 8, you get 24. decrypt it again by dividing it by 3, you get 16, which is the same result you would get if you just multiplied your unencrypted data by 2.

In this example, the encryption method is quite easy to get out of the result, so it’s not secure. But FHE relies on something much more complicated called lattice cryptography, which encodes data as coordinates on a grid. Grids can be thought of as grids of regularly spaced dots, but unlike the 2D grids we are used to, the FHE grids are multidimensional.

Related: The 11 most beautiful math equations

So instead of describing the position of each data point with simple X, Y coordinates, the number of axes can be huge, with each unique piece of data being described by thousands of coordinates. Data points can also be placed between points, so each coordinate can have many decimal places to indicate their precise location. This essentially makes it impossible to crack the coding, even by quantum computers. That’s a promising feature, Rondeau said, because current encryption methods aren’t quantum proof.

FHE relies on a multi-dimensional grid. Credit: ROBERT BROOK / SCIENCE PHOTO LIBRARY via Getty Images
The big problem is that the processing of this data on today’s computers is very slow – about a million times slower than the processing times for unencrypted data. That’s why DARPA has launched a research program called Data Protection in Virtual Environments (DPRIVE), which Rondeau manages, to speed things up. The program recently awarded contracts to an encryption start-up Duality Technologies, software company Galois, non-profit SRI International and a division of Intel called Intel Federal to design new processors and software to increase speeds to just 10 times slower than usual, which is 100,000. times faster than current processing for completely homomorphic encoding.

FHE is so slow because of the way calculations are performed. To complicate matters further, those data points don’t stay static. Researchers found that you can perform math operations such as multiplication or addition by moving data points in the grid. Combining many of these operations allows researchers to perform all kinds of calculations without decoding the data. When you decipher the answer, there is a chance that someone can spy on it; but that answer still wouldn’t reveal anything about the data used to calculate it.

Related: The 9 most massive songs out there

The general problem with this process is that moving accurately placed data points in high-dimensional space is much more complicated than performing calculations with simple binary data – the typical 1’s and 0’s of modern computers.

“It’s this data explosion,” Rondeau told Live Science. “Now, every computation isn’t just manipulating a bit. It’s manipulating all of this information, all of these representations of the dimensions.”

There are two main approaches that the DARPA-funded companies can use to simplify things, Rondeau said. One of the tactics is to improve the computer’s ability to handle very accurate numbers by changing the way numbers are represented in binary code and by modifying chip circuits to process them more efficiently. The other is to translate the data into a lower dimensional space where the calculations are simpler, which also requires new hardware and software approaches.

Each of the teams involved in the program takes a slightly different approach, but Rondeau says he is confident they will achieve the targeted 100,000-fold improvement in processing speeds.

Originally published on Live Science.