[Pictured above: Lattica’s team. Credit: Lattica]
Cryptography startup Lattica has emerged from stealth after completing a $3.25 million pre-seed funding round led by the Fund Cyber venture fund of Konstantin Lomashuk. Operating under the radar for about a year and a half, Lattica was founded by CEO Dr. Rotem Tzabari, who holds a PhD in cryptography from the Weizmann Institute of Science.
The company has developed a cloud platform that enables AI models to run on encrypted data, without the need for prior decryption and without compromising the model’s inference process. This means that users and organizations can utilize AI tools—such as chatbots—while maintaining full privacy over personal and organizational data, an increasingly critical concern in sectors like healthcare, finance, government, and defense. Lattica is currently pursuing a broader funding round to accelerate its market activity. A free demo of its platform is available on the company’s website.
Performing Any Mathematical Operation on Encrypted Data
One of the greatest barriers to the continued growth of the cloud industry lies in data security weaknesses and privacy issues—a central concern in the public cloud space. These concerns have prevented sectors such as finance, insurance, healthcare, and government from large-scale cloud migration. The rise of AI applications, which are often offered via SaaS cloud services, has only intensified this dilemma.
Lattica addresses this challenge using homomorphic encryption, a method distinct from traditional encryption. While standard encryption completely obscures any correlation between the original and encrypted data, homomorphic encryption preserves the mathematical relationships between data elements even under encryption. This makes it possible to perform computations—such as running AI models—on encrypted data and obtain results that are mathematically accurate, without ever revealing the underlying information.
This form of encryption is particularly well-suited to cloud environments, as it allows the cloud to provide data processing services like analytics, AI, and machine learning without removing encryption—followed by decryption of insights only on secure, private servers.
For many years, fully homomorphic encryption (FHE)—enabling any mathematical operation to be performed on encrypted data—was considered a theoretical challenge. In 2009, Craig Gentry of Stanford University presented the first such algorithm, known as Gentry’s Scheme, which proposed a method of “encryption within encryption.” While it was a groundbreaking theoretical advancement, it was extremely slow and impractical for real-world use. Since then, more efficient algorithms have been developed, but not to the point of supporting the high-speed data transmission typical of the digital world.
Most network encryption methods, such as RSA, were developed for and executed on central processing units (CPUs). While FHE algorithms have also traditionally been CPU-based, their computational complexity exceeds the capabilities of standard CPUs to deliver timely results. One attempted solution has been the development of dedicated CPUs for FHE computation.
Rewriting the Algorithms for GPUs
Lattica is taking a different path. Mathematically, these algorithms are actually better suited to execution on graphics processing units (GPUs), due to their ability to perform parallel computations. Lattica has rewritten these algorithms specifically to run in parallel on GPUs, particularly those manufactured by NVIDIA.
In an interview with Techtime, Dr. Tzabari explained: “NVIDIA’s processors brought about a computational revolution. Their compute capabilities drove the huge progress in machine learning and AI. Realizing that these capabilities could also solve the challenge of homomorphic encryption is what led me to found Lattica. NVIDIA offers a wide array of software tools that help maximize the hardware, and we use those tools to accelerate homomorphic encryption. To properly use these accelerators, you need to rewrite the algorithms to run in parallel. We built a solution tailored for NVIDIA processors—there is no longer a need for custom hardware.”