Applied Multiparty Computation and Fully Homomorphic Encryption

 

 

 

(Winter Term 2022/2023)

CISPA / Saarland University

 

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Overview

 

Fully-Homomorphic Encryption (FHE) schemes and Multi-Party Computation (MPC) are fundamental tools in modern cryptography.

For decades FHE and MPC schemes have been abstract concepts living in the realm of cryptographic theory. 

In recent years those systems have seen major improvements in terms of efficiency and practicality.

In short, FHE and MPC have become practical enough to be considered for applications in private delegation of machine learning models and applications to privacy-preserving distributed Genome-wide association studies.

This seminar is concerned with currently implemented applications and practical aspects of FHE and MPC. 

By the end of the seminar, participants should possess fundamental knowledge about FHE and MPC and should know the 
state-of-the-art of libraries and developer tools that are available nowadays. 
In particular, participants should have an overview of what is currently possible to achieve and at what cost using FHE and MPC techniques.

 

Requirements:

During the seminar, students are required to read, understand and present advanced cryptography papers.
Hence, it is required that a student has completed a cryptography course and is fluent in linear and abstract algebra.

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Logistics:

The seminar will take place online. 

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Article Assignments 

 

12:30-14:00, 28 November (Monday) 2022:

1) Private Blocklist Lookups with Checklist

    Presenter:  Rogovskyy, Alexander Jakob Vadymovic

    Reviewers: No Reviews

2) GPU-accelerated PIR with Client-Independent Processing for Large-Scale Applications

    Presenter: Wassmuth, Christian

    Reviewers: Mohd Kashif and Rogovskyy, Alexander Jakob Vadymovic  

12:30-14:00, 05 December (Monday) 2022:

1) Blazing Fast PSI from Improved OKVS and Subfield VOLE

    Presenter: Hasir, Simon Leonard

    Reviewers: Ivanov, Yavor Ivanov and Afonja, Tejumade  

2) Piranha: A GPU Platform for Secure Computation

    Presenter: Schirra, Lars Erik

    Reviewers: Maalej, Majdi and Gräfe Scirgalea, Patrick Alexander  

12:30-14:00,12 December (Monday) 2022:

1) Chaghri - an FHE-friendly Block Cipher

    Presenter: Mohd Kashif

    Reviewers: Wassmuth, Christian and Rogovskyy, Alexander Jakob Vadymovic  

2) Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning

    Presenter: Afonja, Tejumade

    Reviewers: Gräfe Scirgalea, Patrick Alexander and Schirra, Lars Erik 

12:30-14:00,19 December (Monday) 2022:

1) Constant-weight PIR: Single-round Keyword PIR via Constant-weight Equality Operators

    Presenter: Maalej, Majdi

    Reviewers: Schirra, Lars Erik and Afonja, Tejumade  

2) ZeeStar: Private Smart Contracts by Homomorphic Encryption and Zero-knowledge Proofs

    Presenter: Gräfe Scirgalea, Patrick Alexander

    Reviewers: Ivanov, Yavor Ivanov and Hasir, Simon Leonard  

 

12:30-14:00,9 January (Monday) 2023:

1) Simple, Fast Malicious Multiparty Private Set Intersection

    Presenter: Ivanov, Yavor Ivanov

    Reviewers: Maalej, Majdi and Mohd Kashif  

2) Free spot

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Articles: 

Below is a list of articles that students may choose to present. The list is divided into subtopics. 

Other Secure Computation Related:

- Tomer Ashu, Mohammad Mahzoun, Dilara Toprakhisar. Chaghri - an FHE-friendly Block Cipher.

- Marcel Keller. MP-SPDZ: A Versatile Framework for Multi-Party Computation.

- James Bell, K. A. Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova. Secure Single-Server Aggregation with (Poly)Logarithmic Overhead.

- Sahar Mazloom, Phi Hung Le, Samuel Ranellucci, S. Dov Gordon. Secure parallel computation on national scale volumes of data. 

- Weikeng Chen, Raluca Ada Popa. Metal: A Metadata-Hiding File-Sharing System.

- Seung Geol Choi, Dana Dachman-Soled, S. Dov Gordon, Linsheng Liu, and Arkady Yerukhimovich. Compressed Oblivious Encoding for Homomorphically Encrypted Search.

- Dmitry Kogan, Henry Corrigan-Gibbs. Private Blocklist Lookups with Checklist.

 

Collaborative Analytics Related:

- Mahimna Kelkar, Phi Hung Le, Mariana Raykova, and Karn Seth. Secure Poisson Regression.

- Rishabh Poddar, Sukrit Kalra, Avishay Yanai, Ryan Deng, Raluca Ada Popa, and Joseph M. Hellerstein. Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics. 

 

Private Information Retrieval Related:

- Alexandra Henzinger, Matthew M. Hong, Henry Corrigan-Gibbs, Sarah Meiklejohn, Vinod Vaikuntanathan. One Server for the Price of Two: Simple and Fast Single-Server Private Information Retrieval. 

- Yiping Ma, Ke Zhong, Tal Rabin, and Sebastian Angel. Incremental Offline/Online PIR.

- Samir Jordan Menon, David J. Wu. Spiral: Fast, High-Rate Single-Server PIR via FHE Composition.

- Rasoul Akhavan Mahdavi, Florian Kerschbaum. Constant-weight PIR: Single-round Keyword PIR via Constant-weight Equality Operators. 

 

Private Set Intersection:

- Anunay Kulshrestha, Jonathan Mayer. Estimating Incidental Collection in Foreign Intelligence Surveillance: Large-Scale Multiparty Private Set Intersection with Union and Sum. 

- Srinivasan Raghuraman, Peter Rindal. Blazing Fast PSI from Improved OKVS and Subfield VOLE.

- Kelong Cong, Radames Cruz Moreno, Mariana Botelho da Gama, Wei Dai, Ilia Iliashenko, Kim Laine, and Michael Rosenberg. Labeled PSI from Homomorphic Encryption with Reduced Computation and Communication.

- Dung Bui, Geoffroy Couteau. PSI from Ring-OLE.

- Mike Rosulek, Ni Trieu. Compact and Malicious Private Set Intersection for Small Sets.

- Nishanth Chandran, Nishka Dasgupta, Divya Gupta, Sai Lakshmi Bhavana Obbattu, Sruthi Sekar, Akash Shah. Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI.

- Ofri Nevo, Ni Trieu, and Avishay Yanai. Simple, Fast Malicious Multiparty Private Set Intersection.

 

Neural Network Inference and Training Related:

- Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma. CrypTFlow : Secure TensorFlow Inference.

- Zhicong Huang, Wen-jie Lu, Cheng Hong, and Jiansheng Ding. Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference.

- Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. Delphi: A Cryptographic Inference Service for Neural Networks

- Arpita Patra and Ajith Suresh. Blaze: Blazing Fast Privacy Preserving Machine Learning.

- Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, Joao Sa Sousa, Jean-Pierre Hubaux. POSEIDON: Privacy-Preserving Federated Neural Network Learning.

- Ryan Lehmkuhl, Pratyush Mishra, Akshayaram Srinivasan, Raluca Ada Popa. Muse: Secure Inference Resilient to Malicious Clients. 

- Wenting Zheng, Ryan Deng, Weikeng Chen, Raluca Ada Popa, Aurojit Panda, Ion Stoica. Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning

- Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica. Helen: Maliciously Secure Coopetitive Learning for Linear Models.

 

Blockchains:

- Samuel Steffen, Benjamin Bichsel, Roger Baumgartner, Martin Vechev. ZeeStar: Private Smart Contracts by Homomorphic Encryption and Zero-knowledge Proofs. 

- Sean Bowe, Alessandro Chiesa, Matthew Green, Ian Miers, Pratyush Mishra, Howard Wu. ZEXE: Enabling Decentralized Private Computation.

 

GPU acceleration:

- Daniel Günther, Maurice Heymann, Benny Pinkas, Thomas Schneider. GPU-accelerated PIR with Client-Independent Preprocessing for Large- Scale Applications.

- Jean-Luc Watson, Sameer Wagh, Raluca Ada Popa. Piranha: A GPU Platform for Secure Computation.

- Sijun Tan, Brian Knott, Yuan Tian, David J. Wu. CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU.