Understanding Secure (multi-party) computation

[nicola]

Note: this is a bit messy, will be cleaned some time soon

Background

• Privacy-preserving computation: either X of F are hidden to who computes F(X)
• Verifiable computation: A wants B to run F(X)=Y, A wants to prove that B has executed the code correctly.
• Secure multi-party computation: Confidentiality+Integrity of a computation, F(X, Y) X is A's input, Y is B's input, Z runs F.

Lecture Notes on Cryptography

• Lindell's Foundations of cryptography
• Berkley's class on Cryptography
• Chiesa's Crypto class (has also a list of other classes)
• ZK Lecture notes from John Hopkins '07

Lectures at Simons College on Advanced Cryptography

• Cryptography bootcamp: introduction to various concepts
• Mathematics of modern crypto
• Secure computation
• Different topics in crypto
• Computing on Encrypted Data (vinod)
• Universal theory for Computing on Encrypted Data

Areas

Proof of knowledge

• See Understanding SNARKS

Homomorphic encryption

• Eigenvector explanation on FHE
• Homomorphic signatures

AB/P Encryption

• Attribute Based/Predicate Encryption

Garbled Circuits

• Simple introduction (audacity)
• What is a Garbled Circuits
• Practical Garbled Circuits
• Fast Garbled circuits
• Boolean circuits

tm on encrypted data

• How to run Turing Machines on Encrypted data

Verifiable computation

• Finally Pinocchio and Trinocchio explained
• TrueSET: verifiable set computation

Multi-party blockchain-based protocols

• Fair and Robust Multi-Party Computation using a Global Transaction Ledger

[nicola]

Adding Composable SNARKS <- 2015

[nicola]

Adding Hawk: privacy-preserving smart contracts

[nicola]

Adding TrueSET: verifiable set computation <- 2014

[nicola]

Adding Fair and Robust Multi-Party Computation using a Global Transaction Ledger