![]() Bootstrapping is the process in FHE that reduces the noise build-up in a ciphertext after repeated homomorphic operations, and normally represents a major source of computation which is otherwise wasted. The major draw of Concrete lies in the ability to perform what is known as “programmable” bootstrapping. We’re not going to delve too deeply into the maths behind TFHE here as we’ve covered it to a degree in our previous article on the subject, but there are some deeply clever features in Concrete that make it extremely powerful. They’re the developers of a library called Concrete, which provides the programming tools needed to implement an FHE scheme known as TFHE (FHE over the Torus). The third is that Zama, one of the biggest names in FHE, recently released their Concrete-Boolean library. These sources range from full electronic simulation of the optical result through to results generated via linear combinations of real optical data (as used in our paper and previous articles), with the ultimate goal being to provide a connectivity framework and standard interface for our next generation of physical demonstrator chip systems. We’ve described this simulator before in our paper on optical convolutions for neural networks these recent updates were intended to make the simulator more modular, allowing us to use different sources for modelling or executing the core optical Fourier transform. The second is that we’ve finished making some adaptions to our simulator architecture to help us work with optical Fourier transforms. ![]() This in turn ensures that the security properties of these schemes are left untouched by the use of the optical calculation method. ![]() This allows us to apply the optical system to FHE schemes that use these operations to make multiplication much more efficient, without having to make any changes to the mathematics of the scheme (as we did in our last article on the subject, where we converted TFHE into a form that used 2d multinomial operations instead of polynomials). The first is that we’ve finished demonstrating how we can use a core 2-dimensional optical Fourier transform to execute arbitrary Fourier transforms of any size, shape and precision. Several fairly big advances have recently conspired to make these articles possible. This will allow encrypted analysis of text for keywords, and is a precursor to more complex applications such as searching encrypted databases. To highlight the changing face of FHE, in this article we’ll be combining cutting-edge advances in FHE and next-generation optical computing techniques to securely execute a classic example from computer science and complexity theory: Conway’s Game of Life.Īnd in our next article, we’ll be demonstrating how to perform a string search using Concrete Boolean. The field is rapidly evolving, and we’re starting to see the practical groundwork being laid for applications that go beyond limited demonstrations. New tools and new technologies are not just making FHE faster, they’re also making it considerably easier to use. Thus far, the speed of FHE operations has been the main barrier to its uptake (if you need details, we include a summary at the end of this article) It’s simply not fast enough to keep up with the vast amounts of data that need to be handled every single day. ![]() A world in which you can have a smart speaker in your house without worrying about who might be listening.įHE is the solution, but right now there’s a catch: it is incredibly slow relative to unencrypted processing. A world without the endless stream of database breaches or thefts. Imagine a world where organisations can share and collaborate on sensitive data without any risk of it being leaked. Fully Homomorphic Encryption (FHE) offers the ability to perform arbitrary operations on encrypted data, providing an elegant solution to one of the largest and hardest-to-solve security vulnerabilities in cloud computing: the need to decrypt data before processing it. ![]()
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