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ajcr.github.io | ||
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vitalik.eth.limo
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| | | | | [AI summary] The user is interested in understanding polynomial commitments and their applications in privacy-preserving computations, particularly in blockchain. They have provided a detailed overview of FRI, Kate, and bulletproofs, along with finite field arithmetic and encoding computations into polynomial equations. The user is looking for a concise summary of the key points, further clarification on the concepts, and guidance on how to proceed with learning more about the topic. | |
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blog.lambdaclass.com
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| | | | | Introduction The use of efficient zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge) has given rise to many new and vital applications. For example, we can delegate expensive computations to untrusted servers and receive proof showing the integrity of the computations. This proof is short and can be verified much faster | |
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www.jeremykun.com
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| | | | | Problem: Compute the product of two polynomials efficiently. Solution: import numpy from numpy.fft import fft, ifft def poly_mul(p1, p2): """Multiply two polynomials. p1 and p2 are arrays of coefficients in degree-increasing order. """ deg1 = p1.shape[0] - 1 deg2 = p1.shape[0] - 1 # Would be 2*(deg1 + deg2) + 1, but the next-power-of-2 handles the +1 total_num_pts = 2 * (deg1 + deg2) next_power_of_2 = 1 << (total_num_pts - 1). | |
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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. | ||
