Understanding Fully Homomorphic Encryption: Complex Tech With a Simple Proposition

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As a seasoned crypto investor with a background in computer science, I’ve seen my fair share of groundbreaking technologies that promise to revolutionize the digital world. But none have caught my attention quite like Fully Homomorphic Encryption (FHE).

It’s quite challenging to discuss and explain Fully Homomorphic Encryption (FHE) due to its complex name. For this reason, it’s often referred to by its abbreviation instead. While avoiding the nine-syllable title makes conversation easier, understanding the intricacies of how it operates requires a bit more attention.

Regardless of whether you have basic knowledge about cryptography or haven’t performed complex computations before, you can still grasp this point: being capable of examining encrypted data without the need for decryption is quite significant. In fact, if you were the one who invented such technology, you might label it as “revolutionary” and your enthusiasm would be more than justified.

FHE is a big deal for data analysis, for web3, for banking, high-frequency trading, AI and a whole bunch of other digital industries. We’ll examine why this is the case shortly. But first, a quick primer on how FHE works and what distinguishes it from other encryption methods.

What’s the Deal With FHE?

Instead of delving into boolean arithmetic and residue number systems, let’s focus on the key aspects of Fully Homomorphic Encryption (FHE). Essentially, this technology allows for programs to be executed on encrypted data without revealing its contents – a claim that might initially seem unbelievable, but it is indeed true. The magic behind it lies in recent advancements in cryptography and ciphertexts.

Through Fully Homomorphic Encryption (FHE), data is encrypted in a way that allows fundamental mathematical operations like addition and multiplication. For example, if you combine two encrypted messages using FHE, you’ll receive another encrypted message containing the sum of the original plaintexts. Similarly, when these encrypted messages are multiplied together, the result is another encrypted message reflecting the product of the original plaintexts.

Consider a scenario where it’s possible to find the most frequently used password, examine patient records to identify underlying health conditions without revealing confidential data, or analyze credit scores based on demographics without exposing personal details – all while ensuring that the researchers do not gain access to the actual encrypted data, decrypted passwords, sensitive medical information, or personally identifiable data.

Here are some examples of how FHE can be utilized – and it’s worth noting that the potential for AI applications alone could keep FHE analysts working extra hours for the next ten years.

Who’s Using FHE?

Which organizations are currently utilizing Fully Homomorphic Encryption (FHE)? A glance at the list reveals some of the tech giants such as IBM, Microsoft, Oracle, and Google. However, it’s when we delve deeper into the list that we find more specific use cases and intriguing applications. In terms of cybersecurity, FHE is experiencing significant adoption. Interestingly, Alibaba, the company with the most FHE patents filed to date, might come as a surprise.

VentureRadar’s top list for companies utilizing Fully Homomorphic Encryption (FHE) features Fhenix, a web3 innovator aiming to apply this technology for secure on-chain computation of sensitive data. Fhenix has devised a system that allows FHE integration within smart contracts, significantly broadening the possibilities for decentralized apps. Potential applications span across various sectors such as gaming and private social networks.

Beyond the realm of web3, it’s primarily in the automotive sector, cloud services, healthcare, and insurance that Fully Homomorphic Encryption (FHE) is gaining traction. In essence, any industry that relies on data analysis – encompassing nearly all digital domains – can derive significant benefits from its use.

Impediments to Wider Adoption

One way to rephrase the given text could be: If FHE seems so smart, you might ask yourself, why isn’t everyone adopting it? The answer lies in the fact that new and intricate technology often requires time to become fully comprehended and for user-friendly tools to be developed to facilitate its use. Blockchain serves as an excellent illustration: it has been around for 15 years, and most of this period has been spent making it possible for deploying a dapp or appchain as simple as plug-and-play.

It’s worth noting that Fully Homomorphic Encryption (FHE) has been around for as long as blockchain: Craig Gentry introduced the first fully functional FHE scheme in 2009, coincidentally at the same time Satoshi Nakamoto was releasing Bitcoin. Gentry’s design utilized a process called bootstrapping, where the encrypted data is continuously updated to minimize the error that builds up during computations.

Currently, there are technical hurdles that developers of Fully Homomorphic Encryption (FHE) continue to refine solutions for, aiming to boost computational speeds, minimize delays, and improve scalability. Although our understanding of FHE has grown substantially since its inception, many of its practical applications remain theoretical or are under experimental investigation in research environments. However, the number of real-world production implementations of FHE is relatively small but this trend is gradually shifting.

With a vast array of possible applications for FHE and the historical pattern of technologies improving as they mature, it’s logical to anticipate that Fully Homomorphic Encryption will eventually reach its full capacity. Once this happens, those businesses quickest to recognize its potential and create practical solutions for processing encrypted data will be the main beneficiaries.

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2024-10-24 13:24