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www.ayoub-benaissa.com | ||
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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. | |
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jeremykun.wordpress.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.... | |
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programmathically.com
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| | | | | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights [] | |
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scottarc.blog
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| | | This isn't (necessarily) a security vulnerability; merely an observation that I don't think has been articulated adequately within the cryptography community. I thought it would be worth capturing somewhere public so that others can benefit from a small insight when designing cryptosystems. Background Once upon a time, there was Symmetric Encryption, which provided confidentiality, but... | ||
