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jeremykun.wordpress.com | ||
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blog.openmined.org
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| | | | | From the math and the hard problem behind most of today's homomorphic encryption scheme to implementing your own in python. | |
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jeremykun.com
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| | | | | In this article I'll derive a trick used in FHE called sample extraction. In brief, it allows one to partially convert a ciphertext in the Ring Learning With Errors (RLWE) scheme to the Learning With Errors (LWE) scheme. Here are some other articles I've written about other FHE building blocks, though they are not prerequisites... | |
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www.daniellowengrub.com
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| | | | | [AI summary] The text discusses the implementation of homomorphic operations in the context of RLWE (Ring Learning With Errors) and GSW (Gentry-Sahai-Waters) encryption schemes. Key concepts include the use of encryptions of zero to facilitate homomorphic multiplication, the structure of GSW ciphertexts as matrices of RLWE ciphertexts, and the role of scaling factors to manage error growth during multiplication. The main goal is to enable secure computation of polynomial products without revealing the underlying plaintexts. | |
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www.jeremykun.com
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| | | This article was written by my colleague, Cathie Yun. Cathie is an applied cryptographer and security engineer, currently working with me to make fully homomorphic encryption a reality at Google. She's also done a lot of cool stuff with zero knowledge proofs. In previous articles, we've discussed techniques used in Fully Homomorphic Encryption (FHE) schemes. The basis for many FHE schemes, as well as other privacy-preserving protocols, is the Learning With Errors (LWE) problem. | ||
