Cryptoeconomics: Paving the Future of Blockchain Technology | HackerNoon
Page through the articles on <a href="https://hackernoon.com/tagged/blockchain" target="_blank">blockchain</a> today and you will see healthy discussions over its applications and endless debates over the future price action of someone’s favorite cryptocurrency. Yet rarely among all these topics is any mention of the most fascinating notion at the heart of the movement — blockchain as a novel means<strong> to incentivize desired human behavior</strong>.
The Latecomer’s Guide to Crypto (Published 2022)
Crypto is a lot of things – including terribly explained. We’re here to clear things up.
CryptPad: end-to-end encrypted collaboration suite
CryptPad: end-to-end encrypted collaboration suite
CryptPad: end-to-end encrypted collaboration suite
CryptPad: end-to-end encrypted collaboration suite
Crypto 101
Crypto 101 is an introductory course on cryptography, freely available for programmers of all ages and skill levels.
Architecture
Overview of Ente's end-to-end encrypted architecture—learn how your data is encrypted on-device, securely shared, and safely stored using cryptography.
CIDs: What You Need to Know and Why, Part 1 - Nick's Blog
A deep dive into Content Identifiers, the self-describing cryptographic fingerprints that form the foundation of ATProtocol’s data model.
Noble cryptography
Paul Miller. I make projects which help developers to build awesome things
The (Edited) Latecomer's Guide to Crypto
A group of cryptocurrency researchers and critics annotate the irresponsible cryptocurrency puff piece that was originally published in the New York Times.
ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
As large language models (LLMs) are used in sensitive fields, accurately verifying their computational provenance without disclosing their training datasets poses a significant challenge, particularly in regulated sectors such as healthcare, which have strict requirements for dataset use. Traditional approaches either incur substantial computational cost to fully verify the entire training process or leak unauthorized information to the verifier. Therefore, we introduce ZKPROV, a novel cryptographic framework allowing users to verify that the LLM's responses to their prompts are trained on datasets certified by the authorities that own them. Additionally, it ensures that the dataset's content is relevant to the users' queries without revealing sensitive information about the datasets or the model parameters. ZKPROV offers a unique balance between privacy and efficiency by binding training datasets, model parameters, and responses, while also attaching zero-knowledge proofs to the responses generated by the LLM to validate these claims. Our experimental results demonstrate sublinear scaling for generating and verifying these proofs, with end-to-end overhead under 3.3 seconds for models up to 8B parameters, presenting a practical solution for real-world applications. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.
Encrypted Spaces — Research preview
An architecture for collaborative applications where data is encrypted and operations are cryptographically verifiable.
Encryption + Trust & Safety reading list (updated 2026-05-20)
CyberChef
The Cyber Swiss Army Knife - a web app for encryption, encoding, compression and data analysis