tl;dr
The article discusses the growing interest in Fully Homomorphic Encryption (FHE) and its potential to revolutionize industries by allowing data to be processed without decryption. It explores the development, challenges, and practical applications of FHE, as well as advancements and future goals for...
Interest in FHE (fully homomorphic encryption) is growing as companies seek more robust data privacy solutions in an increasingly regulated world. FHE allows data to be processed without ever being decrypted – a breakthrough that could revolutionize industries where data security is paramount. To delve deeper into this technology, I’m planning to answer some of the most asked questions about FHE, what it is capable of today and its future potential to redefine secure data processing. What inspired the development of FHE Homomorphic encryption developed gradually over the past several decades – first by the accidental discovery of partially homomorphic systems and then more purposefully until its full capability emerged in 2009 and the following decade. The implications of these developments were staggering – we could send data to the cloud, an AI engine or another third party for processing without ever worrying about a resulting privacy breach. However, the computational power required to implement FHE remained many orders of magnitude greater than computing ‘in the clear,’ making broad adoption a difficult sell, and relegating FHE to an academic endeavor. Now, however, interest in and advancement of FHE is driven by new forces. Companies must navigate a complex legal framework encompassing GDPR in Europe, CCPA in California and diverse regulations in at least 14 other US states. And yet, the commercial appetite for third-party data continues to grow – companies are counting on the ability to ingest new data in order to solve hard problems ranging from detecting financial fraud to researching medical treatments. At the same time, privacy-assuring alternatives to FHE face significant headwinds. Confidential computing methods such as TEEs (trusted execution environments) have been shown time and again to be vulnerable to both side-channel attacks and direct breaches, placing the companies that rely on them at risk. Other privacy-assuring approaches, such as secure multi-party computation, typically require networks of computers to be and stay online together throughout computations – requiring complex network configurations and vulnerable to failure if any one of the participating machines or network links fails. FHE, on the other hand, has cryptographically sound proofs of privacy, requires no complex network configurations and relies only on a single compute server’s reliability. This pairing of cryptographically strong privacy guarantees with simplicity of deployment makes FHE a strong contender for practical, secure privacy assurance in fields such as finance and healthcare, where privacy is paramount. With FHE, companies can perform computations on encrypted data, ensuring that data remains protected throughout storage, transit and processing. Now, we’re at the forefront of a new wave of hardware accelerators that will take FHE the last mile to commercial performance viability. We’re on the brink of a whole new era in data privacy. Within a generation, there will be no such thing as sharing or outsourcing computation on unencrypted data. Can you explain the concept of computing on encrypted data and why it is considered a breakthrough in data privacy In the past, we’ve encrypted data at rest – in storage media such as disk drives – and in transit on networks. However, to process data, we needed to decrypt it, because no practical encryption mechanisms also allowed computation. Decrypting the data also made it visible to anyone performing that computation, requiring the data’s owner to trust those performing the computation. Novel encryption schemes, such as those used in FHE, not only keep the data from being revealed, but also allow computation on the data in its encrypted state. As a result, data owners need not trust those performing computations to keep the data private. This ‘zero trust, full computation’ breakthrough is a sea change in the relationship between data owner and data processor, enabling outsourcing of computation without risk of data compromise. What are the main challenges associated with implementing FHE in real-world applications I see three main challenges. 1. The computational complexity of FHE is a performance challenge FHE computations are dramatically slower than unencrypted computations, often by several orders of magnitude, making it difficult to achieve practical performance levels. This slowdown is due to the additional work required by CPUs and GPUs to manage the complicated data representations used in FHE. 2. The data expansion typically seen in FHE encryptions is a storage and network bandwidth challenge Homomorphically encrypted data is also substantially larger than unencrypted data, requiring times more storage space. Current research ideas such as hybrid FHE are insufficiently developed to answer this challenge so far. 3. The complex algorithms required to compute in FHE are a usability challenge Programming in FHE – even with the advent of some fantastic FHE libraries – is a major challenge because of the many parameters that must be correctly chosen for FHE, and because of the many auxiliary operations needed to manage FHE computations, which (due to lack of tooling) cannot be automatically handled by the programmer’s tools. How does the process of encrypting data for FHE work, and what role does homomorphism play in this process To answer that question in full
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