Our intern for 2022, Peter Li, was a second-year (now third-year) undergraduate studying engineering at Cambridge University. Peter’s work focused on the deployment of common machine learning models in the FHE space.
For data scientists looking to make the transition into working safely with highly sensitive information, this article features examples of the implementation and execution of several different machine learning techniques in encrypted space.
When companies collaborate on data, there’s a tension between the benefits of collaboration and the risks to confidentiality. Data has value because it relates to something. Remove that relationship in the interests of protection, and you remove the value. Preserve that relationship, and the risks associated with exposure run higher.
But what if there was a way of resolving this tension? What kind of a world would we see, and what would this mean for businesses and organisations?
As we move forwards with developments in this revolutionary new field of cyber-security, now is a good time to revisit what it is that Optalysys does and consolidate updates on how our technology has been shaped in the context of this development.
In this article, we show how to compute the maximum and minimum of an encrypted array of data without decrypting it, using Zama’s Concrete implementation of the TFHE programmable bootstrapping.
Optalysys has announced that its E series of optical processing technologies enable Fully Homomorphic Encryption (FHE) by significantly reducing the time taken to perform Fourier Transform functions.
The list of things we want to keep secret is nearly endless, yet how much attention do we pay to this process? After all, big corporations or governments are handling it for us on secure systems, so shouldn’t our data be fully protected?
After 2 days of intensive and competitive pitching sessions, Optalysys has been selected from a pool of over 300 candidates who presented their business at the online pitching sessions.
But FHE is very slow, making it unusable for most applications. The solution is acceleration via dedicated hardware designed to accelerate the most intensive operation. Optalysys’ Echip technology can provide an order-of-magnitude acceleration in homomorphic calculations.
Computing at the speed of light has been a long time coming, but a new generation of optical processors promise to be faster – and cooler – than electronic incumbents.
By uniquely combining the high speed modulation of silicon photonics, with the parallel processing of free space optics, Optalysys is developing a novel chiplet-based technology to provide new levels of performance and efficiency for Fourier based processes across AI, homomorphic / post quantum encryption and other numerical modelling applications.