Advances in Randomness and Hashing

The field of randomness and hashing is moving towards more efficient and secure constructions. Recent developments have focused on improving the size and error of min-wise hash families, as well as exploring new approaches to random number generation. Notably, researchers are investigating the use of pseudorandomness and lazy evaluation to simulate infinite randomness with finite information. Additionally, the capabilities and limitations of large language models in handling randomness are being studied. Overall, the field is advancing towards more innovative and effective solutions for generating and utilizing random numbers. Noteworthy papers include: Explicit Min-wise Hash Families with Optimal Size, which presents the first explicit min-wise hash families with optimal size and sub-constant error. The Beautiful Deception: How 256 Bits Pretend to be Infinity, which explores the fundamental deception at the heart of computational cryptography and demonstrates how 256 bits of entropy can generate sequences indistinguishable from infinite randomness.

Sources

Explicit Min-wise Hash Families with Optimal Size

Adding All Flavors: A Hybrid Random Number Generator for dApps and Web3

Evaluating the Quality of Randomness and Entropy in Tasks Supported by Large Language Models

The Beautiful Deception: How 256 Bits Pretend to be Infinity

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