- Precomputing Oblivious Transfer, CRYPTO'95, Bea95
- Efficient Oblivious Transfer Protocols, SODA'01, NP01
- Extending Oblivious Transfers Efficiently, CRYPTO'03, IKNP03
- More Efficient Oblivious Transfer and Extensions for Faster Secure Computation, CCS'13, slide, ALSZ13
- Improved OT Extension for Transferring Short Secrets, CRYPTO'13, KK13
- Actively Secure OT Extension with Optimal Overhead, CRYPTO'15, KOS15
- 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
- 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
- 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
- How to play ANY mental game, STOC'87, GMW
- Scalable and unconditionally secure multiparty computation, CRYPTO'07
- Perfectly-secure MPC with linear communication complexity, TCC'08
- Sharemind: A framework for fast privacy-preserving computations, ESORICS'08
- Multiparty Computation from Somewhat Homomorphic Encryption, IACR ePrint'11
- Practical Covertly Secure MPC for Dishonest Majority Or: Breaking the SPDZ Limits, ESORICS'13
- 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
- A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority, CCS'17
- SPDZ2k: Efficient MPC mod 2k for Dishonest Majority, CRYPTO'18
- Yet another compiler for active security or: Efficient MPC over arbitrary rings, CRYPTO'18
- Overdrive^2k: Making SPDZ Great Again, Eurocrypto'18
- An end-to-end system for large scale P2P MPC-as-a-service and low-bandwidth MPC for weak participants, CCS'18
- Fast large-scale honest-majority MPC for malicious adversaries, CRYPTO'18
- Minimising communication in honest-majority MPC by batchwise multiplication verification, ACNS'18
- Two-thirds honest-majority MPC for malicious adversaries at almost the cost of semi-honest, CCS'19
- Efficient Information-Theoretic Secure Multiparty Computation over Z/pkZ via Galois Rings, TCC'19
- Malicious Security Comes Free in Honest-Majority MPC, IACR ePrint'20
- Use Your Brain! Arithmetic 3PC for Any Modulus with Active Security, ITC'20
- ATLAS: Efficient and Scalable MPC in the Honest Majority Setting, CRYPTO'21
- The Cost of IEEE Arithmetic in Secure Computation, LatinCrypt'21
- Rabbit: Efficient Comparison for Secure Multi-Party Computation, FC'21
- Honest Majority MPC with Abort with Minimal Online Communication, Latincrypt'21
- CostCO: An automatic cost modeling framework for secure multi-party computation, Euro S&P'22
- Fast Fully Secure Multi-Party Computation over Any Ring with Two-Thirds Honest Majority, CCS'22
- More Efficient Dishonest Majority Secure Computation over Z2k via Galois Rings, CRYPTO'22
- 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
- Improved primitives for mpc over mixed arithmetic-binary circuits, CRYPTO'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
- Faster Private Set Intersection based on OT Extension, USENIX Security'14, code: 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
- Practical Multi-party Private Set Intersection from Symmetric-Key Techniques, CCS'17, code: MultipartyPSI
- Scalable Private Set Intersection Based on OT Extension, TOPS'18
- Labeled PSI from Fully Homomorphic Encryption with Malicious Security, CCS'18
- An Algebraic Approach to Maliciously Secure Private Set Intersection, EUROCRYPT'19
- SpOT-Light: Lightweight Private Set Intersection from Sparse OT Extension, CRYPTO'19
- PSI from PaXoS: Fast, Malicious Private Set Intersection, EUROCRYPT'20
- Private Set Intersection in the Internet Setting from Lightweight Oblivious PRF, CRYPTO'20
- Labeled PSI from homomorphic encryption with reduced computation and communication, CCS'21
- Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI, CCS'21
- VOLE-PSI: Fast OPRF and Circuit-PSI from Vector-OLE, EUROCRYPT'21
- Private Set Operations from Oblivious Switching, PKC'21
- Multi-party Threshold Private Set Intersection with Sublinear Communication, PKC'21
- Oblivious Key-Value Stores and Amplification for Private Set Intersection, CRYPTO'21
- Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF, PoPETS'22
- Structure-Aware Private Set Intersection, With Applications to Fuzzy Matching, CRYPTO'22, code: FuzzyPSI
- Blazing Fast PSI from Improved OKVS and Subfield VOLE, ePrint'22
- A Plug-n-Play Framework for Scaling Private Set Intersection to Billion-sized Sets, ePrint'22
- LibPSI
- Fast Secure Multiparty ECDSA with Practical Distributed Key Generation and Applications to Cryptocurrency Custody, CCS'18, code: Blockchain-Crypto-MPC
- Threshold ECDSA from ECDSA Assumptions: The Multiparty Case, S&P'19, code: MPECDSA
- Secure Computation with Preprocessing via Function Secret Sharing, TCC'19
- Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation, EUROCRYPT'21
- Secure Multiparty Computation (MPC), Yehuda Lindell.
- How to Simulate It - A Tutorial on the Simulation Proof Technique, Yehuda Lindell.
- Secure Multi-Party Computation, Manoj Prabhakaran, Amit Sahai.
- An Introduction to Secret-Sharing-Based Secure Multiparty Computation, Daniel Escudero.
- 实用安全多方计算协议关键技术研究进展, 计算机研究与发展'15
- The Foundations of Cryptography - Volume 1: Basic Tools, Oded Goldreich. 2001.
- The Foundations of Cryptography - Volume 2: Basic Applications, Oded Goldreich. 2003.
- Efficient secure two-party protocols: Techniques and constructions, Carmit Hazay, Yehuda Lindell. 2010.
- Engineering Secure Two-Party Computation Protocols, Thomas Schneider. 2012.
- Secure Multiparty Computation and Secret Sharing, Ronald Cramer, Ivan Bjerre Damgård, Jesper Buus Nielsen. 2015.
- Applications of Secure Multiparty Computation, Peeter Laud, Liina Kamm. 2015.
- A Pragmatic Introduction to Secure Multi-Party Computation, David Evans, Vladimir Kolesnikov, Mike Rosulek. 2018.
- 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 12th BIU Winter School on Cryptography
- The Universal Composability 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
- 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
- FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection, DASFAA'20
- Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning, USENIX ATC'21
- 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
| Title | Venue | |
|---|---|---|
| VC3: trustworthy data analytics in the cloud using SGX | S&P'15 | link |
| Ryoan: a distributed sandbox for untrusted computation on secret data. | OSDI'16 | link |
| Securing Data Analytics on SGX With Randomization | ESORICS'17 | link |
| STANlite - a database engine for secure data processing at rack-scale level | IC2E'18 | link |
| Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware | ICLR'19 | link |
| Using Trusted Execution Environments for Secure Stream Processing of Medical Data | DAIS'19 | link |
| Computation on Encrypted Data using Dataflow Authentication | PETS'20 | link |
| secureTF: A Secure TensorFlow Framework | Middleware'20 | link |
| Vessels: Efficient and Scalable Deep Learning Prediction on Trusted Processors | SoCC'20 | link |
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.
- HEhub - A library for homomorphic encryption and its 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.
- 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'20
- 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
- OpenFHE: Open-Source Fully Homomorphic Encryption Library, ePrint'22
- Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias, JASA'65
- Mechanism Design via Differential Privacy, FOCS'07
- How Much Is Enough? Choosing ε for Differential Privacy, ISC'11
- Differentially Private Empirical Risk Minimization, JMLR'11
- Personal privacy vs population privacy, KDD'11
- Functional Mechanism: Regression Analysis under Differential Privacy, VLDB'12
- Stochastic gradient descent with differentially private updates, GlobalSIP'13
- RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response, CCS'14
- Efficient Per-Example Gradient Computations, arXiv'15
- Privacy-Preserving Deep Learning, CCS'15
- Concentrated Differential Privacy, arXiv'16
- Deep Learning with Differential Privacy, CCS'16
- Differentially Private Password Frequency Lists, NDSS'16
- Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds, TCC'16
- Rényi Differential Privacy, CSF'17
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, ICLR'17
- Locally differentially private protocols for frequency estimation, USENIX Security'17
- Understanding the sparse vector technique for differential privacy, VLDB'17
- Detecting Violations of Differential Privacy, CCS'18
- Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting, CSF'18
- Privacy Amplification by Iteration, FOCS'18
- Learning Differentially Private Recurrent Language Models, ICLR'18
- Scalable private learning with pate, ICLR'18
- Differential Privacy: A Primer for a Non-Technical Audience, SSRN'18
- Rényi Differential Privacy of the Sampled Gaussian Mechanism, arXiv'19
- That which we call private, arXiv'19
- Differential Privacy in Practice: Expose your Epsilons!, JPC'19
- Understanding Gradient Clipping in Private SGD: A Geometric Perspective, NeurIPS'20
- Locally Differentially Private Frequency Estimation with Consistency, NDSS'20
- Automatic Discovery of Privacy–Utility Pareto Fronts, PETS'20
- Differential Privacy in the Shuffle Model: A Survey of Separations, arXiv'21
- Tempered Sigmoid Activations for Deep Learning with Differential Privacy, AAAI'21
- Differentially Private Learning Needs Better Features (or Much More Data), ICLR'21
- Differentially Private Learning with Adaptive Clipping, NeurIPS'21
- Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization, NeurIPS'21
- Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping, PETS'21
- Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger, arXiv'22
- Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy, NeurIPS'22
- The Algorithmic Foundations of Differential Privacy, by Cynthia Dwork and Aaron Roth
- The Complexity of Differential Privacy, by Salil Vadhan
- Differential Privacy: From Theory to Practice, by Ninghui Li, Min Lyu, Dong Su, Weining Yang
- Algorithms for Private Data Analysis, taught by Gautam Kamath, Youtube, Bilibili
- Privacy in Statistics and Machine Learning, taught by Adam Smith and Jonathan Ullman
- TensorFlow Privacy - Training TensorFlow models with differential privacy
- Opacus - Training PyTorch models with differential privacy
- Google DP Library - Google's differential privacy libraries
- IBM DP Library - IBM's differential privacy library
- PyDP - OpenMined's Python DP library built on top of Google's
- PipelineDP - OpenMined's library for applying DP aggregations to large datasets
- OpenDP - A modular collection of algorithms for building privacy-preserving applications
- Machine Learning Classification over Encrypted Data, NDSS'14
- Oblivious Multi-Party Machine Learning on Trusted Processors, USENIX SECURITY'16
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, ICML'16
- CryptoDL: Deep Neural Networks over Encrypted Data, arXiv'17
- 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
- PySyft: A Generic Framework for Privacy Preserving Deep Learning, arXiv'18
- ABY3: A Mixed protocol Framework for Machine Learning, CCS'18
- SecureNN: Efficient and Private Neural Network Training, PoPETs'18
- Gazelle: A Low Latency Framework for Secure Neural Network Inference, USENIX SECURITY'18
- Private Machine Learning in TensorFlow using Secure Computation, arXiv'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
- ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction, CCSW'19
- SoK: Modular and Efficient Private Decision Tree Evaluation, PoPETs'19
- Garbled Neural Networks are Practical, IACR ePrint'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, PoPETs'20
- Secure Evaluation of Quantized Neural Networks, PoPETs'20
- Delphi: A Cryptographic Inference Service for Neural Networks, USENIX SECURITY'20
- MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference, ARES'20
- SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search, USENIX Security'20
- CrypTen: Secure multi-party computation meets machine learning, NeurIPS'20
- An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference, ESORICS'20
- Secure and Verifiable Inference in Deep Neural Networks, ACSAC'20
- Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data, NeurIPS'20
- CrypTFlow: Secure TensorFlow Inference, S&P'20
- CrypTFlow2: Practical 2-Party Secure Inference, CCS'20
- ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing, arXiv'20
- Practical Privacy-Preserving K-means Clustering, PoPETs'20
- ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (Full Version), USENIX Security'21
- SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning, USENIX Security'21
- 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, USENIX Security'21
- DeepReDuce: ReLU Reduction for Fast Private Inference, USENIX Security'21
- 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
- Circa: Stochastic ReLUs for Private Deep Learning, NeurIPS'21
- Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning, USENIX Security'21
- FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning, PoPETs'21
- SoK: Efficient Privacy-preserving Clustering, PoPETs'21
- ZEN: Efficient Zero-Knowledge Proofs for Neural Networks, IACR ePrint'21
- zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy, CCS'21
- Adam in Private : Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation, arXiv'21
- Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning, USENIX Security'21
- Tetrad: Actively Secure 4PC for Secure Training and Inference, NDSS'22
- SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost, USENIX Security'22
- SIMC 2.0: Improved Secure ML Inference Against Malicious Clients, arXiv'22
- 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
- Pika: Secure Computation using Function Secret Sharing over Rings, PoPETs'22
- Piranha: A GPU platform for secure computation, USENIX Security'22
- Secure Quantized Training for Deep Learning, ICML'22
- Prio+: Privacy Preserving Aggregate Statistics via Boolean Shares, ePrint'22
- Microsoft Research. Videos from SEAL/CKKS talks at Microsoft's Private AI Bootcamp.