yuzhangshlg/awesome-privacy-computing

Awesome Privacy Computing

1 Secure Multiparty Computation (SMPC)

1.1 Primitive

1.1.1 Oblivious Transfer (OT)

• Precomputing Oblivious Transfer, CRYPTO'95
• Efficient Oblivious Transfer Protocols, SODA'01
• IKNP03: Extending Oblivious Transfers Efficiently, CRYPTO'03
• ALSZ13: More Efficient Oblivious Transfer and Extensions for Faster Secure Computation, CCS'13, slide
• KK13: Improved OT Extension for Transferring Short Secrets, CRYPTO'13
• KOS15: Actively Secure OT Extension with Optimal Overhead, CRYPTO'15
• MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer, CCS'16
• Fast Actively Secure OT Extension for Short Secrets, NDSS'17, slide, video
• Efficient Pseudorandom Correlation Generators: Silent OT Extension and More, CRYPTO'19
• Efficient two-round OT extension and silent non-interactive secure computation, CCS'19
• Ferret: Fast Extension for Correlated OT with Small Communication, CCS'20
• Silver: Silent VOLE and Oblivious Transfer from Hardness of Decoding Structured LDPC Codes, CRYPTO'21

1.1.2 Garbled Circuit

• Protocols for Secure Computations (Extended Abstract), FOCS'82
• How to generate and exchange secrets, FOCS'86
• Improved Garbled Circuit: Free XOR Gates and Applications, ICALP'08
• FairplayMP – A System for Secure Multi-Party Computation, CCS'08
• Secure Two-Party Computation Is Practical, ASIACRYPT'09
• Foundations of Garbled Circuits, CCS'12
• FleXOR: Flexible Garbling for XOR Gates That Beats Free-XOR, CRYPTO'14
• Two Halves Make a Whole: Reducing Data Transfer in Garbled Circuits using Half Gates, EUROCRYPT'15
• MRZ15: Fast and Secure Three-party Computation: The Garbled Circuit Approach, CCS'15
• Three Halves Make a Whole? Beating the Half-Gates Lower Bound for Garbled Circuits, CRYPTO'21

1.1.3 Arithmetic/Boolean Circuit

• GMW: How to play ANY mental game, STOC'87
• Multiparty Computation from Somewhat Homomorphic Encryption
• Practical Covertly Secure MPC for Dishonest Majority Or: Breaking the SPDZ Limits
• SPDZ2k: Efficient MPC mod 2k for Dishonest Majority
• ATLAS: Efficient and Scalable MPC in the Honest Majority Setting, CRYPTO'21
• The Cost of IEEE Arithmetic in Secure Computation, LatinCrypt'21
• Fast Fully Secure Multi-Party Computation over Any Ring with Two-Thirds Honest Majority, CCS'22
• A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority, CCS'17
• Overdrive^2k: Making SPDZ Great Again, Eurocrypto'18
• Scalable and unconditionally secure multiparty computation, Crypto'07
• Sharemind: A framework for fast privacy-preserving computations, ESORICS'08
• Fast large-scale honest-majority MPC for malicious adversaries, Crypto'18
• High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority, CCS'16
• High-throughput secure three-party computation for malicious adversaries and an honest majority, Crypto'17

1.1.5 A/B/Y Shares Conversion

• ABY – A Framework for Effificient Mixed-Protocol Secure Two-Party Computation, NDSS'15
• ABY3 : A Mixed Protocol Framework for Machine Learning, CCS'18
• Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, NDSS'20
• MP-SPDZ: A versatile framework for multi-party computation, CCS'20
• ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation, USENIX Security'21
• MOTION – A Framework for Mixed-Protocol Multi-Party Computation, TOPS'22
• Tetrad: Actively Secure 4PC for Secure Training and Inference, NDSS'22
• Improved primitives for mpc over mixed arithmetic-binary circuits, CRYPTO'20

1.1.6 PSI

• Efficient Batched Oblivious PRF with Applications to Private Set Intersection, CCS'16, code: BaRK-OPRF
• Actively Secure 1-out-of-N OT Extension with Application to Private Set Intersection, CT-RSA'17
• SpOT-Light: Lightweight Private Set Intersection from Sparse OT Extension, CRYPTO'19
• PSI from PaXoS: Fast, Malicious Private Set Intersection, EUROCRYPT'20
• VOLE-PSI: Fast OPRF and Circuit-PSI from Vector-OLE, EUROCRYPT'21
• LibPSI
• PSI, 2014/447.

1.1.7 Multiparty ECDSA signing

• Blockchain-Crypto-MPC, CCS'18
• MPECDSA, S&P'19

1.2 Survey

• Yehuda Lindell. Secure Multiparty Computation (MPC)
• Yehuda Lindell. How to Simulate It - A Tutorial on the Simulation Proof Technique
• Manoj Prabhakaran and Amit Sahai (Eds.) Secure Multi-Party Computation

1.3 Books

• A Pragmatic Introduction to Secure Multi-Party Computation

• Applications of Secure Multiparty Computation

• Efficient Secure Two-Party Protocols

• Secure Multiparty Computation and Secret Sharing

• Foundations of Cryptography Vol. 2

• Engineering Secure Two-Party Computation Protocols

1.4 Courses

• Cryptographic Computing Course
• FHE-MPC Advanced Grad Course
• Secure Computation
• Secure Multi-Party Computation at Scale
• The 1st BIU Winter School on Secure Computation and Efficiency
• The 5th BIU Winter School on Advances in Practical Multiparty Computation
• The Universal Composability Framework

1.5 Open Source Framework

• ABY, NDSS'15.
• ABY3, CCS'18, 2019/518.
• BatchDualEx, eprint: 2016/632.
• CrypTen, link
• EMP-toolkit, (emp-[ag2pc|m2pc|agmpc]) | eprint: 2017/189, 2016/762, 2017/030.
• Fancy-Garbling, 2016/969.
• FRESCO , Practice'15.
• HoneyBadgerMPC
• JIFF, link.
• MP-SPDZ, documentation | eprint: 2020/512
• MPyC, TPMPC'18.
• Obliv-C, 2015/1153.
• SCALE-MAMBA, link.
• Sharemind, Cyber'13.
• swanky, Tf-encrypted

2 Federated Learning (FL)

• Privacy-Preserving Deep Learning, CCS'15
• Practical Secure Aggregation for Privacy Preserving Machine Learning, CCS'17
• Privacy-Preserving Deep Learning via Additively Homomorphic Encryption, TIFS'17
• NIKE-based Fast Privacy-preserving High-dimensional Data Aggregation for Mobile Devices, CACR'18
• PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks, CCSW'19
• VerifyNet: Secure and verifiable federated learning, TIFS'19
• PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
• NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning, TDSC'20
• SAFER: Sparse secure Aggregation for FEderated leaRning
• Secure Byzantine-Robust Machine Learning
• Secure Single-Server Aggregation with (Poly)Logarithmic Overhead, CCS'20
• Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning, USENIX ATC'21
• FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection, DASFAA'20
• FLGUARD: Secure and Private Federated Learning, Cryptology Eprint'21
• Biscotti: A Blockchain System for Private and Secure Federated Learning, TPDS'21
• POSEIDON: Privacy-Preserving Federated Neural Network Learning, NDSS'21

3 Trusted Execution Environment (TEE)

4 Homomorphic Encryption (HE)

FHE Libraries

Libraries that can be used to implement applications using (Fully) Homomorphic Encryption.

• Microsoft SEAL - C++ FHE library implementing BFV and CKKS schemes.
• HEAAN - Scheme with native support for fixed point approximate arithmetic.
• HElib - BGV scheme with bootstrapping and the Approximate Number CKKS scheme.
• lattigo - Go library for lattice-based crypto that implements various schemes.
• PALISADE - lattice encryption library.
• tfhe - Faster fully HE: Bootstrapping in less than 0.1 seconds.
• FHEW - A Fully HE library based on FHEW: Bootstrapping Homomorphic Encryption in less than a second.
• concrete - Rust FHE library that implements Zama's variant of TFHE.
• Cupcake - Facebook's Rust library for the (additive version of the) Fan-Vercauteren scheme.

FHE Applications

• OpenMined - Decentralized data ownership & intelligence based on HE and deep / federated learning.
  • KotlinSyft - Kotlin library for the Android part of the OpenMined's open-source ecosystem.
  • PySyft - Python library for the server/IoT part of the OpenMined's open-source ecosystem.
  • SwiftSyft - Swift library for the iOS part of the OpenMined's open-source ecosystem.
  • syft.js - JavaScript library for the web part of the OpenMined's open-source ecosystem.
• Rosetta - A privacy-preserving framework based on TensorFlow.
• tf-encrypted - Bridge between TensorFlow and the Microsoft SEAL library.

FHE Papers

• Fully homomorphic encryption using ideal lattices, STOC'99.
• Fully homomorphic encryption from ring-LWE and security for key dependent messages, CRYPTO'11.
• Homomorphic Evaluation of the AES Circuit, CRYPTO'12.
• Fully homomorphic encryption with polylog overhead, EUROCRYPT'12.
• Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP, CRYPTO'12.
• Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based, CRYPTO'13
• Algorithms in HElib, CRYPTO'14
• FHEW: Bootstrapping Homomorphic Encryption in Less Than a Second, EUROCRYPT'15
• Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds, ASIACRYPT'16
• Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE, ASIACRYPT'17
• Homomorphic Encryption for Arithmetic of Approximate Numbers, ASIACRYPT'17
• A Full RNS Variant of FV Like Somewhat Homomorphic Encryption Schemes, SAC'17
• Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE, ASIACRYPT'17
• Faster homomorphic linear transformations in HElib, CRYPTO'18
• Bootstrapping for Approximate Homomorphic Encryption, EUROCRYPT'18
• An Improved RNS Variant of the BFV Homomorphic Encryption Scheme, CT-RSA'19
• TFHE: Fast Fully Homomorphic Encryption Over the Torus, JOC'20
• Efficient Homomorphic Comparison Methods with Optimal Complexity, ASIACRYPT'2020
• PEGASUS: Bridging polynomial and non-polynomial evaluations in homomorphic encryption, S&P'21
• General Bootstrapping Approach for RLWE-based Homomorphic Encryption, ePrint'21
• On the Security of Homomorphic Encryption on Approximate Numbers, EUROCRYPT'21
• Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys, EUROCRYPT'21
• Efficient Homomorphic Conversion Between (Ring) LWE Ciphertexts, ACNS'21

5 Differential Privacy (DP)

6 Zero-Knowledge Proof (ZKP)

7 Privacy-Preserving Machine Learning (PPML)

• Machine Learning Classification over Encrypted Data, NDSS'14
• Oblivious Multi-Party Machine Learning on Trusted Processors, USENIX SECURITY'16
• Prio: Private, Robust, and Scalable Computation of Aggregate Statistics, NSDI'17
• SecureML: A System for Scalable Privacy-Preserving Machine Learning, S&P'17
• MiniONN: Oblivious Neural Network Predictions via MiniONN Transformations, CCS'17
• Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, AsiaCCS'17
• DeepSecure: Scalable Provably-Secure Deep Learning, DAC'17
• Secure Computation for Machine Learning With SPDZ, NIPS'18
• ABY3:a Mixed protocol Framework for Machine Learning, CCS'18
• SecureNN: Efficient and Private Neural Network Training, PETS'18
• Gazelle: A Low Latency Framework for Secure Neural Network Inference, USENIX SECURITY'18
• CHET: an optimizing compiler for fully-homomorphic neural-network inferencing, PLDI'19
• New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning, S&P'19
• Helen: Maliciously Secure Coopetitive Learning for Linear Models, S&P'19
• Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. CCS'19
• XONN: XNOR-based Oblivious Deep Neural Network Inference, USENIX Security'19
• QUOTIENT: two-party secure neural network training and prediction, CCS'19
• Secure Evaluation of Quantized Neural Networks, PETS'20
• ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction, CCSW'19
• SoK: Modular and Efficient Private Decision Tree Evaluation, PETS'19
• Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, NDSS'20
• BLAZE: Blazing Fast Privacy-Preserving Machine Learning, NDSS'20
• FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning, PETS'20
• Delphi: A Cryptographic Inference Service for Neural Networks, USENIX SECURITY'20
• FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning, PETS'21
• MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference, ARES'20
• SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search, USENIX Security'20
• PySyft: A Generic Framework for Privacy Preserving Deep Learning
• Private Deep Learning in TensorFlow Using Secure Computation
• CryptoDL: Deep Neural Networks over Encrypted Data
• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
• CrypTFlow: Secure TensorFlow Inference
• CrypTFlow2: Practical 2-Party Secure Inference, CCS'20
• ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
• Practical Privacy-Preserving K-means Clustering, PETS'20
• ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (Full Version), USENIX Security'21
• SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
• An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference, ESORICS'20
• Secure and Verifiable Inference in Deep Neural Networks, ACSAC'20
• Privacy-preserving Density-based Clustering, AisaCCS'21
• SIRNN: A Math Library for Secure RNN Inference, S&P'21
• Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation, NDSS'21
• MUSE: Secure Inference Resilient to Malicious Clients
• DeepReDuce: ReLU Reduction for Fast Private Inference, USENIX Security'21
• Garbled Neural Networks are Practical
• GForce : GPU-Friendly Oblivious and Rapid Neural Network Inference, USENIX Security'21
• CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU, S&P'21
• GALA : Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks, NDSS'21
• Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security, USENIX Security'21
• When homomorphic encryption marries secret sharing: secure large-scale sparse logistic regression and applications in risk control, KDD'21
• Microsoft Research. Videos from SEAL/CKKS talks at Microsoft's Private AI Bootcamp.
• Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data, NeurIPS'20
• Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning, USENIX Security'21
• SoK: Efficient Privacy-preserving Clustering, PoPETs'21
• ZEN: Efficient Zero-Knowledge Proofs for Neural Networks
• zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy, CCS'21
• Secure Quantized Training for Deep Learning
• Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning, USENIX Security'21
• Tetrad: Actively Secure 4PC for Secure Training and Inference, NDSS'22
• Adam in Private : Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation
• SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost, USENIX Security'22
• Circa : Stochastic ReLUs for Private Deep Learning, NeurIPS'21
• Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference, USENIX Security'22
• Secure Poisson Regression, USENIX Security'22
• SecFloat: Accurate Floating-Point meets Secure 2-Party Computation, S&P'22
• MPClan: Protocol Suite for Privacy-Conscious Computations, IACR ePrint'22
• LLAMA: A Low Latency Math Library for Secure Inference, PoPETs'22