Cryptography

Sunscreen FHE: Private Information Retrieval via Matrix Operations

Sunscreen's documentation on Private Information Retrieval via Fully Homomorphic Encryption — demonstrating how FHE enables querying a database without the server learning what you searched for. A practical introduction to FHE programming via a concrete PIR example.

The MoonMath Manual: A Practitioner's Guide to Zero-Knowledge Proof Systems

The MoonMath Manual is a comprehensive, example-driven introduction to zero-knowledge proof systems, covering the mathematics from finite fields through elliptic curves to Groth16 and PLONK. Written to be accessible to practitioners without a cryptography PhD, it became a widely-used self-study resource for the ZK ecosystem.

Town Crier — Authenticated Data Feed for Smart Contracts

Town Crier is an authenticated data feed system for smart contracts that uses Intel SGX trusted execution environments to fetch external data with cryptographic attestation. An early, rigorous oracle design predating Chainlink's dominance.

Signing Git Commits with Your SSH Key

A walkthrough of using your existing SSH key to sign Git commits instead of a GPG key — a feature added in Git 2.34. Signing git commits is good practice for supply chain security, and SSH keys are already part of most developers' daily workflow, making this a low-friction upgrade over GPG.

Piranha: A GPU Platform for Secure Computation

Piranha (USENIX Security 2022) is a GPU platform for secure multi-party computation that exploits GPU parallelism to accelerate MPC protocols by 10-15× over CPU implementations. It shows that GPU hardware can close the practical performance gap for privacy-preserving machine learning at scale.

Why and How zk-SNARK Works

Maksym Petkus provides a pedagogical ground-up explanation of zk-SNARKs, answering not just how they work but why each component exists. Structured to build from first principles with no cryptography prerequisites, making it the standard accessible reference for developers wanting to understand the math behind zkEVM and privacy-preserving protocols.

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Networks

This paper demonstrates running deep neural network inference entirely on encrypted data using fully homomorphic encryption, so the server never sees plaintext inputs or outputs. It makes encrypted ML inference practical by combining FHE with approximation-friendly neural network architectures.

ZK: Zero to Hero

A curated learning path for understanding zero-knowledge proofs from scratch — covering the math foundations, SNARK/STARK constructions, and practical applications in blockchain. One of the more organized community-built ZK curricula from 2022.

Proofs, Arguments, and Zero-Knowledge

Clockwork Finance: Automated Analysis of Economic Security in Smart Contracts

Clockwork Finance Framework (CFF) is a formal verification system for reasoning about economic security in DeFi smart contracts, introducing 'extractable value' (EV) as a rigorous notion. Applied to Uniswap, Sushiswap, and MakerDAO, it automatically discovers $56M/month in MEV-like opportunities.

Secure Multiparty Computation (MPC)

Yehuda Lindell's accessible overview of secure multiparty computation (MPC) — how parties can jointly compute on private inputs without revealing them. Traces the field from Yao's two-party garbled circuits through modern practical protocols used in industry.

CrypTen: Secure Multi-Party Computation Meets Machine Learning

CrypTen is a PyTorch-compatible framework from Facebook AI Research that wraps secure multi-party computation protocols behind a familiar tensor API, making private inference and training accessible to ML practitioners without cryptography expertise. The bet is that adoption bottlenecks for privacy-preserving ML are mostly about developer experience, not theoretical limits.

Secure Byzantine-Robust Machine Learning

He, Karimireddy, and Jaggi propose a two-server cryptographic protocol that simultaneously achieves Byzantine robustness, input privacy, and local differential privacy for distributed machine learning — three properties usually addressed separately. Bridges the privacy-robustness gap in federated learning.

Programming Bitcoin — Jimmy Song

Jimmy Song's 'Programming Bitcoin' book repository — a hands-on O'Reilly book that teaches Bitcoin internals by implementing the cryptography and protocol layer from scratch in Python. The deepest technical introduction to how Bitcoin actually works.

btc-heist — Bitcoin Key Generation Educational Tool

btc-heist is a toy Bitcoin key generation tool that generates random private/public key pairs and checks if the address matches any in a list of addresses with non-zero balances. An educational demonstration of why Bitcoin's 2^256 key space makes brute-force impossible in practice.

Voynich Manuscript: Word Vectors and t-SNE Visualization

Christian Perone applies word2vec embeddings and t-SNE visualization to the Voynich Manuscript — an undeciphered 15th-century text — to surface structural patterns in its unknown script. A creative application of NLP tools to a centuries-old mystery.

The Matasano Crypto Challenges

Maciej Cegłowski's Pinboard post recommending the Matasano Crypto Challenges — a set of progressively harder cryptography exercises that teach you to break real cryptographic constructions. The most effective way to understand why cryptography is hard.