Homomorphic Encryption Lets Computers Process Data Without Ever Decrypting It
Homomorphic encryption (HE) allows computations to be performed directly on encrypted data, returning a result that matches what would be obtained from unencrypted input — eliminating the vulnerable decryption step. The concept was first posed by Rivest, Adleman, and Dertouzos in 1978, but a working fully homomorphic solution was only achieved in 2009 when Stanford researcher Craig Gentry developed a bootstrapping technique to prevent noise buildup in ciphertexts. Modern schemes such as BFV, BGV, and CKKS have since optimized the technology for different use cases, with CKKS becoming the standard for machine learning on encrypted data. Open-source libraries from Microsoft and IBM, released in 2018, made the technology practically accessible for real-world applications including encrypted fraud detection, genomic research, and secure vote tallying. Despite dramatic performance improvements, fully homomorphic encryption remains slower than plaintext computation, with bootstrapping still its most resource-intensive step.
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