Instructor

Dr.Yupeng Zhang

Office Location: HRBB 414A

Office Hours: By Appointment

Lectures

Time: TR 12:45PM - 2:00PM

Location: HBRR 126

Course Description and Prerequisites

This course covers techniques in applied cryptography and their applications in encrypted search, machine learning and blockchain to enhance data privacy. Related cryptographic techniques include searchable encryption, secure multiparty computation, verifiable computation and zero knowledge proof. We will discuss their basic concepts and state-of-the-art constructions. Additionally, we will talk about how to use these techniques to construct privacy-preserving machine learning, crypto-currencies and blockchain. We will focus on efficiency and functionality constraints in practice, and discuss challenges and solutions to efficiently realize these cryptographic protocols.

Course Overview

Suggested topics for projects:

Secure Multiparty Computations

• Privacy-preserving decision trees and random forest training and/or predictions: apply MPC techniques to train decision tree and random forest models on encrypted data. 1. Understand decision tree and random forest. 2. Collect datasets and implement training and predictions on plaintext data. 3. Use only those computations efficiently supported by MPC, compare the accuracy to the baseline. 4. Implement the MPC protocol using existing libraries.
• Privacy-preserving SVM training and/or predictions: apply MPC techniques to train SVM models on encrypted data. 1. Understand SVM. 2. Collect datasets and implement training and predictions on plaintext data. 3. Use only those computations efficiently supported by MPC, compare the accuracy to the baseline. 4. Implement the MPC protocol using existing libraries.

Searchable Encryptions

• Leakage abuse attacks of searchable encryption on real-world datasets. 1. Understand the leakage of SE and existing attacks utilizing the leakage. 2. Find larger and more realistic datasets of emails or files. 3. Implement these attacks on new datasets and compare to the original results

Zero Knowledge Proof

• Privacy-preserving smart contracts. a1. Understand the mechanism of smart contract. 2. Find commonly used smart contracts on existing blockchains and cryptocurrencies. 3. Given general purpose ZKP, design protocols for privacy-preserving smart contracts. 4. Implement the ZKP protocol using existing libraries and optimize for those commonly used smart contracts.
• Information inference from public data on Ethereum. 1. Understand the public data posted on the blockchain of Ethereum and figure out ways to download the data. 2. Repeat data analysis from existing papers. 3. Design new attacks to infer sensitive information from the public data.
• Zero knowledge proof for machine learning model predictions: generate a proof that the predictions of a secret model on a public testing dataset reaches certain accuracy. Design efficient ZKP protocols for convolution, neural networks and common activation functions.
• Linear time zero knowledge proof for polynomial evaluations.
• Contingent payment on blockchain with zero knowledge proof.

Grading Policies:

Class participation(10%):Students are encouraged to participate discussions during the lectures.

Reading assignment(25%)Students will submit reviews for one of the reading materials every week.

Project(65%): Students will form groups and complete research projects related to the topics of the course

Piazza: