The safety and security of critical information – whether it is sensitive intellectual property (IP), financial information, personally identifiable information (PII), intelligence insight, or beyond – is of vital importance. Conventional data encryption methods or cryptographic solutions, such as Advanced Encryption Standards (AES), translate data into a secret “code” that can only be decoded by people with access to a decryption key. These methods protect data as it is transmitted across a network or at rest while in storage. Processing or computing on this data however requires that it is first decrypted, exposing it to numerous vulnerabilities and threats. Fully homomorphic encryption (FHE) offers a solution to this challenge. FHE enables computation on encrypted data, or ciphertext, rather than plaintext, or unencrypted data – essentially keeping data protected at all times. The benefits of FHE are significant, from enabling the use of untrusted networks to enhancing data privacy. Despite its potential, FHE requires enormous computation time to perform even simple operations, making it exceedingly impractical to implement with traditional processing hardware.
FHE relies on a particular type of cryptography called lattice cryptography, which presents complex mathematical challenges to would-be attackers that require technologies beyond the current state of the art to solve. While effective at keeping data protected, the challenge with modern lattice-based FHE is the unavoidable accumulation of noise with each calculation performed. With each homomorphic computation, a certain amount of noise – or error – is generated that corrupts the encrypted data representation. Once this noise accumulation reaches a certain point, it becomes impossible to recover the original underlying plaintext. Essentially, the data in need of protection is now lost. Computational structures called “bootstrapping” help address this untenable noise accumulation, reducing it to a level that is comparable to the original plaintext, but produces massive compute overhead to perform.
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