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Beating the Half-Gates Lower Bound for Garbled Circuits, CRYPTO'21](https://eprint.iacr.org/2021/749.pdf) 34 | 35 | #### 1.1.3 Arithmetic/Boolean Circuit 36 | 37 | - [How to play ANY mental game, STOC'87](https://www.researchgate.net/publication/234778924_How_to_play_ANY_mental_game/link/0deec5232112523fc5000000/download), GMW 38 | - [Scalable and unconditionally secure multiparty computation, CRYPTO'07](https://www.iacr.org/archive/crypto2007/46220565/46220565.pdf) 39 | - [Perfectly-secure MPC with linear communication complexity, TCC'08](https://link.springer.com/chapter/10.1007/978-3-540-78524-8_13) 40 | - [Sharemind: A framework for fast privacy-preserving computations, ESORICS'08](https://link.springer.com/chapter/10.1007/978-3-540-88313-5_13) 41 | - [Multiparty Computation from Somewhat Homomorphic Encryption, IACR ePrint'11](https://eprint.iacr.org/2011/535.pdf) 42 | - [Practical Covertly Secure MPC for Dishonest Majority Or: Breaking the SPDZ Limits, ESORICS'13](https://eprint.iacr.org/2012/642.pdf) 43 | - [High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority, CCS'16](https://dl.acm.org/citation.cfm?id=2978331) 44 | - [High-throughput secure three-party computation for malicious adversaries and an honest majority, CRYPTO'17](https://eprint.iacr.org/2016/944.pdf) 45 | - [A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority, CCS'17](https://eprint.iacr.org/2017/816.pdf) 46 | - [SPDZ2k: Efficient MPC mod 2k for Dishonest Majority, CRYPTO'18](https://eprint.iacr.org/2018/482.pdf) 47 | - [Yet another compiler for active security or: Efficient MPC over arbitrary rings, CRYPTO'18](https://eprint.iacr.org/2017/908) 48 | - [Overdrive^2k: Making SPDZ Great Again, Eurocrypto'18](https://eprint.iacr.org/2017/1230) 49 | - [An end-to-end system for large scale P2P MPC-as-a-service and low-bandwidth MPC for weak participants, CCS'18](https://eprint.iacr.org/2018/751.pdf) 50 | - [Fast large-scale honest-majority MPC for malicious adversaries, CRYPTO'18](https://eprint.iacr.org/2018/570) 51 | - [Minimising communication in honest-majority MPC by batchwise multiplication verification, ACNS'18](https://eprint.iacr.org/2018/474) 52 | - [Two-thirds honest-majority MPC for malicious adversaries at almost the cost of semi-honest, CCS'19](https://dl.acm.org/doi/10.1145/3319535.3339811) 53 | - [Efficient Information-Theoretic Secure Multiparty Computation over Z/pkZ via Galois Rings, TCC'19](https://eprint.iacr.org/2019/872) 54 | - [Malicious Security Comes Free in Honest-Majority MPC, IACR ePrint'20](https://eprint.iacr.org/2020/134) 55 | - [Use Your Brain! Arithmetic 3PC for Any Modulus with Active Security, ITC'20](https://eprint.iacr.org/2019/164.pdf) 56 | - [ATLAS: Efficient and Scalable MPC in the Honest Majority Setting, CRYPTO'21](https://eprint.iacr.org/2021/833) 57 | - [The Cost of IEEE Arithmetic in Secure Computation, LatinCrypt'21](https://eprint.iacr.org/2021/054) 58 | - [Rabbit: Efficient Comparison for Secure Multi-Party Computation, FC'21](https://eprint.iacr.org/2021/119) 59 | - [Honest Majority MPC with Abort with Minimal Online Communication, Latincrypt'21](https://eprint.iacr.org/2020/1556) 60 | - [CostCO: An automatic cost modeling framework for secure multi-party computation, Euro S&P'22](https://eprint.iacr.org/2022/332) 61 | - [Fast Fully Secure Multi-Party Computation over Any Ring with Two-Thirds Honest Majority, CCS'22](https://eprint.iacr.org/2022/623) 62 | - [More Efficient Dishonest Majority Secure Computation over Z2k via Galois Rings, CRYPTO'22](https://eprint.iacr.org/2022/815.pdf) 63 | 64 | #### 1.1.5 A/B/Y Shares Conversion 65 | 66 | - [ABY: A Framework for Effificient Mixed-Protocol Secure Two-Party Computation, NDSS'15](https://encrypto.de/papers/DSZ15.pdf) 67 | - [ABY3: A Mixed Protocol Framework for Machine Learning, CCS'18](https://eprint.iacr.org/2018/403.pdf) 68 | - [Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, NDSS'20](https://eprint.iacr.org/2019/1315) 69 | - [MP-SPDZ: A versatile framework for multi-party computation, CCS'20](https://eprint.iacr.org/2020/521.pdf) 70 | - [Improved primitives for mpc over mixed arithmetic-binary circuits, CRYPTO'20](https://eprint.iacr.org/2020/338.pdf) 71 | - [ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation, USENIX Security'21](https://eprint.iacr.org/2020/1225.pdf) 72 | - [MOTION – A Framework for Mixed-Protocol Multi-Party Computation, TOPS'22](https://eprint.iacr.org/2020/1137) 73 | - [Tetrad: Actively Secure 4PC for Secure Training and Inference, NDSS'22](https://arxiv.org/abs/2106.02850) 74 | 75 | 76 | #### 1.1.6 PSI 77 | 78 | - [Faster Private Set Intersection based on OT Extension, USENIX Security'14](https://eprint.iacr.org/2014/447.pdf), [code: PSI](https://github.com/encryptogroup/PSI) 79 | - [Efficient Batched Oblivious PRF with Applications to Private Set Intersection, CCS'16](https://eprint.iacr.org/2016/799.pdf), [code: BaRK-OPRF](https://github.com/osu-crypto/BaRK-OPRF) 80 | - [Actively Secure 1-out-of-N OT Extension with Application to Private Set Intersection, CT-RSA'17](https://eprint.iacr.org/2016/933.pdf) 81 | - [Practical Multi-party Private Set Intersection from Symmetric-Key Techniques, CCS'17](https://eprint.iacr.org/2017/799.pdf), [code: MultipartyPSI](https://github.com/osu-crypto/MultipartyPSI) 82 | - [Scalable Private Set Intersection Based on OT Extension, TOPS'18](https://eprint.iacr.org/2016/930.pdf) 83 | - [Labeled PSI from Fully Homomorphic Encryption with Malicious Security, CCS'18](https://eprint.iacr.org/2018/787.pdf) 84 | - [An Algebraic Approach to Maliciously Secure Private Set Intersection, EUROCRYPT'19](https://eprint.iacr.org/2017/1064.pdf) 85 | - [SpOT-Light: Lightweight Private Set Intersection from Sparse OT Extension, CRYPTO'19](https://eprint.iacr.org/2019/634.pdf) 86 | - [PSI from PaXoS: Fast, Malicious Private Set Intersection, EUROCRYPT'20](https://eprint.iacr.org/2020/193.pdf) 87 | - [Private Set Intersection in the Internet Setting from Lightweight Oblivious PRF, CRYPTO'20](https://eprint.iacr.org/2020/729.pdf) 88 | - [Labeled PSI from homomorphic encryption with reduced computation and communication, CCS'21](https://eprint.iacr.org/2021/1116.pdf) 89 | - [Efficient Linear Multiparty PSI and Extensions to Circuit/Quorum PSI, CCS'21](https://eprint.iacr.org/2021/172.pdf) 90 | - [VOLE-PSI: Fast OPRF and Circuit-PSI from Vector-OLE, EUROCRYPT'21](https://eprint.iacr.org/2021/266.pdf) 91 | - [Private Set Operations from Oblivious Switching, PKC'21](https://eprint.iacr.org/2021/243.pdf) 92 | - [Multi-party Threshold Private Set Intersection with Sublinear Communication, PKC'21](https://eprint.iacr.org/2020/600) 93 | - [Oblivious Key-Value Stores and Amplification for Private Set Intersection, CRYPTO'21](https://eprint.iacr.org/2021/883.pdf) 94 | - [Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF, PoPETS'22](https://petsymposium.org/2022/files/papers/issue1/popets-2022-0018.pdf) 95 | - [Structure-Aware Private Set Intersection, With Applications to Fuzzy Matching, CRYPTO'22](https://eprint.iacr.org/2022/1011), [code: FuzzyPSI](https://github.com/osu-crypto/FuzzyPSI) 96 | - [Blazing Fast PSI from Improved OKVS and Subfield VOLE, ePrint'22](https://eprint.iacr.org/2022/320) 97 | - [A Plug-n-Play Framework for Scaling Private Set Intersection to Billion-sized Sets, ePrint'22](https://eprint.iacr.org/2022/294) 98 | - [LibPSI](https://github.com/osu-crypto/libPSI) 99 | 100 | #### 1.1.7 PIR (Private Information Retrieval) 101 | 102 | - [Private Information Retrieval, JACM'97](https://dl.acm.org/doi/10.1145/293347.293350) 103 | - [XPIR: Private Information Retrieval for Everyone, PETS'16](https://eprint.iacr.org/2014/1025) 104 | - [PIR with Compressed Queries and Amortized Query Processing, S&P'18](https://ieeexplore.ieee.org/document/8418648), [code: SealPIR](https://github.com/microsoft/SEALpir) 105 | - [Private Stateful Information Retrieval, CCS'18](https://dl.acm.org/doi/10.1145/3243734.3243821) 106 | - [SHECS-PIR: Somewhat Homomorphic Encryption-Based Compact and Scalable Private Information Retrieval, ESORICS 2020](https://dl.acm.org/doi/abs/10.1007/978-3-030-59013-0_5) 107 | - [Communication–Computation Trade-offs in PIR, USENIX Security'21](https://www.usenix.org/conference/usenixsecurity21/presentation/ali) 108 | - [Constant-weight PIR: Constant-weight PIR: Single-round Keyword PIR via Constant-weight Equality Operators, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/mahdavi), [code](https://github.com/RasoulAM/constant-weight-pir) 109 | - [OnionPIR: OnionPIR: Response Efficient Single-Server PIR, CCS'21](https://dl.acm.org/doi/10.1145/3460120.3485381) 110 | - [Pantheon: Private Retrieval from Public Key-Value Store, VLDB'22](https://dl.acm.org/doi/10.14778/3574245.3574251) 111 | - [One Server for the Price of Two: Simple and Fast Single-Server Private Information Retrieval, USENIX Security'23](https://www.usenix.org/system/files/sec23summer_27-henzinger-prepub.pdf), [code](https://github.com/ahenzinger/simplepir) 112 | - [FrodoPIR: Simple, Scalable, Single-Server Private Information Retrieval, PETS'23](https://petsymposium.org/popets/2023/popets-2023-0022.pdf), [code](https://github.com/brave-experiments/frodo-pir) 113 | 114 | #### 1.1.8 Multiparty ECDSA signing 115 | - [Fast Secure Multiparty ECDSA with Practical Distributed Key Generation and Applications to Cryptocurrency Custody, CCS'18](https://eprint.iacr.org/2018/987), [code: Blockchain-Crypto-MPC](https://github.com/unbound-tech/blockchain-crypto-mpc) 116 | - [Threshold ECDSA from ECDSA Assumptions: The Multiparty Case, S&P'19](https://eprint.iacr.org/2019/523), [code: MPECDSA](https://gitlab.com/neucrypt/mpecdsa) 117 | 118 | #### 1.1.9 Function Secret Sharing 119 | - [Secure Computation with Preprocessing via Function Secret Sharing, TCC'19](https://link.springer.com/content/pdf/10.1007/978-3-030-36030-6_14.pdf) 120 | - [Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation, EUROCRYPT'21](https://link.springer.com/content/pdf/10.1007/978-3-030-77886-6_30.pdf) 121 | 122 | ## 1.2 Survey 123 | 124 | - [Secure Multiparty Computation (MPC)](https://eprint.iacr.org/2020/300.pdf), Yehuda Lindell. 125 | - [How to Simulate It - A Tutorial on the Simulation Proof Technique](https://eprint.iacr.org/2016/046.pdf), Yehuda Lindell. 126 | - [Secure Multi-Party Computation](http://ebooks.iospress.com/volume/secure-multi-party-computation), Manoj Prabhakaran, Amit Sahai. 127 | - [An Introduction to Secret-Sharing-Based Secure Multiparty Computation](https://eprint.iacr.org/2022/062.pdf), Daniel Escudero. 128 | - [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](https://crad.ict.ac.cn/CN/10.7544/issn1000-1239.2015.20150763) 129 | 130 | ## 1.3 Books 131 | 132 | - [The Foundations of Cryptography - Volume 1: Basic Tools](https://www.wisdom.weizmann.ac.il/~oded/foc-vol1.html), Oded Goldreich. 2001. 133 | - [The Foundations of Cryptography - Volume 2: Basic Applications](http://www.wisdom.weizmann.ac.il/~oded/foc-vol2.html), Oded Goldreich. 2003. 134 | - [Efficient secure two-party protocols: Techniques and constructions](https://www.springer.com/us/book/9783642143021), Carmit Hazay, Yehuda Lindell. 2010. 135 | - [Engineering Secure Two-Party Computation Protocols](https://www.sites.google.com/site/thomaschneider/publications/engineeringsfebook), Thomas Schneider. 2012. 136 | - [Secure Multiparty Computation and Secret Sharing](http://www.cambridge.org/dk/academic/subjects/computer-science/cryptography-cryptology-and-coding/secure-multiparty-computation-and-secret-sharing?format=HB), Ronald Cramer, Ivan Bjerre Damgård, Jesper Buus Nielsen. 2015. 137 | - [Applications of Secure Multiparty Computation](http://ebooks.iospress.nl/volume/applications-of-secure-multiparty-computation), Peeter Laud, Liina Kamm. 2015. 138 | - [A Pragmatic Introduction to Secure Multi-Party Computation](https://securecomputation.org/), David Evans, Vladimir Kolesnikov, Mike Rosulek. 2018. 139 | 140 | 141 | ## 1.4 Courses 142 | 143 | - [Cryptographic Computing Course](http://orlandi.dk/crycom) 144 | - [FHE-MPC Advanced Grad Course](https://homes.esat.kuleuven.be/~nsmart/FHE-MPC/) 145 | - [Secure Computation](http://drona.csa.iisc.ernet.in/~arpita/SecureComputation15.html) 146 | - [Secure Multi-Party Computation at Scale](https://piazza.com/bu/fall2017/cs591v1/home) 147 | - [The 1st BIU Winter School on Secure Computation and Efficiency](https://cyber.biu.ac.il/event/the-1st-biu-winter-school/) 148 | - [The 5th BIU Winter School on Advances in Practical Multiparty Computation](https://cyber.biu.ac.il/event/the-5th-biu-winter-school/) 149 | - [The 12th BIU Winter School on Cryptography](https://cyber.biu.ac.il/event/the-12th-biu-winter-school-on-cryptography/) 150 | - [The Universal Composability Framework](https://m.youtube.com/playlist?list=PLqc9MPlwib9nSuyH4oUIwPsyDiZ4bwuEE) 151 | 152 | 153 | # 1.5 Open Source Framework 154 | 155 | - [ABY](https://github.com/encryptogroup/ABY), [NDSS'15](http://encrypto.de/papers/DSZ15.pdf). 156 | - [ABY3](https://github.com/ladnir/aby3), [CCS'18](https://eprint.iacr.org/2018/403.pdf), [2019/518](https://eprint.iacr.org/2019/518.pdf). 157 | - [BatchDualEx](https://github.com/osu-crypto/batchDualEx), eprint: [2016/632](https://eprint.iacr.org/2016/632). 158 | - [CrypTen](https://github.com/facebookresearch/CrypTen), [link](https://crypten.ai/) 159 | - [EMP-toolkit](https://github.com/emp-toolkit), (emp-[ag2pc|m2pc|agmpc]) | eprint: [2017/189](https://eprint.iacr.org/2017/189), [2016/762](https://eprint.iacr.org/2016/762), [2017/030](https://eprint.iacr.org/2017/030). 160 | - [Fancy-Garbling](https://github.com/spaceships/fancy-garbling), [2016/969](https://eprint.iacr.org/2016/969). 161 | - [FRESCO](http://fresco.readthedocs.io/en/latest/) , [Practice'15](http://practice-project.eu/downloads/publications/D22.1-State-of-the-art-analysis-PU-V1.1.pdf). 162 | - [HoneyBadgerMPC](https://github.com/initc3/HoneyBadgerMPC) 163 | - [JIFF](https://github.com/multiparty/jiff/), [link](https://multiparty.org/jiff/). 164 | - [MP-SPDZ](https://github.com/data61/MP-SPDZ), [documentation](https://mp-spdz.readthedocs.io/en/latest/) | eprint: [2020/512](https://eprint.iacr.org/2020/521) 165 | - [MPyC](https://www.win.tue.nl/~berry/mpyc/), [TPMPC'18](https://www.win.tue.nl/~berry/mpyc/TPMPC2018.pdf). 166 | - [Obliv-C](http://oblivc.org/), [2015/1153](http://eprint.iacr.org/2015/1153). 167 | - [SCALE-MAMBA](https://homes.esat.kuleuven.be/~nsmart/SCALE/), [link](https://homes.esat.kuleuven.be/~nsmart/SCALE/Documentation.pdf). 168 | - [Sharemind](https://sharemind.cyber.ee/), [Cyber'13](https://cyber.ee/research/theses/roman_jagomagis_msc.pdf). 169 | - [swanky](https://github.com/GaloisInc/swanky), [Tf-encrypted](https://github.com/mortendahl/tf-encrypted/) 170 | 171 | 172 | # 2 Federated Learning (FL) 173 | 174 | * [Privacy-Preserving Deep Learning, CCS'15](https://dl.acm.org/citation.cfm?id=2813687) 175 | * [Practical Secure Aggregation for Privacy Preserving Machine Learning, CCS'17](https://eprint.iacr.org/2017/281.pdf) 176 | * [Privacy-Preserving Deep Learning via Additively Homomorphic Encryption, TIFS'17](https://ieeexplore.ieee.org/document/8241854) 177 | * [NIKE-based Fast Privacy-preserving High-dimensional Data Aggregation for Mobile Devices, CACR'18](http://cacr.uwaterloo.ca/techreports/2018/cacr2018-10.pdf) 178 | * [PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks, CCSW'19](https://eprint.iacr.org/2019/979.pdf) 179 | * [VerifyNet: Secure and verifiable federated learning, TIFS'19](https://ieeexplore.ieee.org/abstract/document/8765347) 180 | * [PrivColl: Practical Privacy-Preserving Collaborative Machine Learning](https://link.springer.com/chapter/10.1007/978-3-030-58951-6_20) 181 | * [NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning, TDSC'20](https://ieeexplore.ieee.org/abstract/document/8981947) 182 | * [SAFER: Sparse secure Aggregation for FEderated leaRning](https://arxiv.org/abs/2007.14861) 183 | * [Secure Byzantine-Robust Machine Learning](https://arxiv.org/abs/2006.04747) 184 | * [Secure Single-Server Aggregation with (Poly)Logarithmic Overhead, CCS'20](https://eprint.iacr.org/2020/704.pdf) 185 | * [FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection, DASFAA'20](https://arxiv.org/abs/2003.10637) 186 | * [Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning, USENIX ATC'21](https://www.usenix.org/conference/atc20/presentation/zhang-chengliang) 187 | * [FLGUARD: Secure and Private Federated Learning, Cryptology Eprint'21](https://eprint.iacr.org/2021/025) 188 | * [Biscotti: A Blockchain System for Private and Secure Federated Learning, TPDS'21](https://ieeexplore.ieee.org/document/9292450) 189 | * [POSEIDON: Privacy-Preserving Federated Neural Network Learning, NDSS'21](https://arxiv.org/abs/2009.00349) 190 | 191 | 192 | # 3 Trusted Execution Environment (TEE) 193 | 194 | # 4 Homomorphic Encryption (HE) 195 | 196 | ## 4.1 FHE Libraries 197 | 198 | Libraries that can be used to implement applications using (Fully) Homomorphic Encryption. 199 | - [Microsoft SEAL](https://github.com/microsoft/SEAL) - C++ FHE library implementing BFV and CKKS schemes. 200 | - [HEAAN](https://github.com/snucrypto/HEAAN) - Scheme with native support for fixed point approximate arithmetic. 201 | - [HElib](https://github.com/HomEnc/HElib) - BGV scheme with bootstrapping and the Approximate Number CKKS scheme. 202 | - [lattigo](https://github.com/ldsec/lattigo) - Go library for lattice-based crypto that implements various schemes. 203 | - [PALISADE](https://palisade-crypto.org/software-library) - lattice encryption library. 204 | - [tfhe](https://github.com/tfhe/tfhe) - Faster fully HE: Bootstrapping in less than 0.1 seconds. 205 | - [FHEW](https://github.com/lducas/FHEW) - A Fully HE library based on [_FHEW: Bootstrapping Homomorphic Encryption in less than a second_](https://eprint.iacr.org/2014/816). 206 | - [concrete](https://github.com/zama-ai/concrete) - Rust FHE library that implements Zama's variant of TFHE. 207 | - [Cupcake](https://github.com/facebookresearch/Cupcake) - Facebook's Rust library for the (additive version of the) Fan-Vercauteren scheme. 208 | - [HEhub](https://github.com/primihub/hehub) - A library for homomorphic encryption and its applications 209 | 210 | ## 4.2 FHE Applications 211 | - [OpenMined](https://github.com/OpenMined) - Decentralized data ownership & intelligence based on HE and deep / federated learning. 212 | - [KotlinSyft](https://github.com/OpenMined/KotlinSyft) - Kotlin library for the Android part of the OpenMined's open-source ecosystem. 213 | - [PySyft](https://github.com/OpenMined/PySyft) - Python library for the server/IoT part of the OpenMined's open-source ecosystem. 214 | - [SwiftSyft](https://github.com/OpenMined/SwiftSyft) - Swift library for the iOS part of the OpenMined's open-source ecosystem. 215 | - [syft.js](https://github.com/OpenMined/syft.js) - JavaScript library for the web part of the OpenMined's open-source ecosystem. 216 | - [Rosetta](https://github.com/LatticeX-Foundation/Rosetta) - A privacy-preserving framework based on TensorFlow. 217 | - [tf-encrypted](https://github.com/tf-encrypted/tf-encrypted) - Bridge between TensorFlow and the [Microsoft SEAL](#SEAL) library. 218 | 219 | ## 4.3 FHE Papers 220 | 221 | - [Fully homomorphic encryption using ideal lattices, STOC'99](https://dl.acm.org/doi/10.1145/1536414.1536440). 222 | - [Fully homomorphic encryption from ring-LWE and security for key dependent messages, CRYPTO'11](http://link.springer.com/10.1007/978-3-642-22792-9_29). 223 | - [Homomorphic Evaluation of the AES Circuit, CRYPTO'12](https://link.springer.com/chapter/10.1007/978-3-642-32009-5_49). 224 | - [Fully homomorphic encryption with polylog overhead, EUROCRYPT'12](https://link.springer.com/chapter/10.1007/978-3-642-29011-4_28). 225 | - [Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP, CRYPTO'12](http://link.springer.com/10.1007/978-3-642-32009-5_50). 226 | - [Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based, CRYPTO'13](http://link.springer.com/10.1007/978-3-642-40041-4_5) 227 | - [Algorithms in HElib, CRYPTO'14](http://link.springer.com/10.1007/978-3-662-44371-2_31) 228 | - [FHEW: Bootstrapping Homomorphic Encryption in Less Than a Second, EUROCRYPT'15](http://link.springer.com/10.1007/978-3-662-46800-5_24) 229 | - [Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds, ASIACRYPT'16](http://link.springer.com/10.1007/978-3-662-53887-6_1) 230 | - [Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE, ASIACRYPT'17](http://link.springer.com/10.1007/978-3-319-70694-8_14) 231 | - [Homomorphic Encryption for Arithmetic of Approximate Numbers, ASIACRYPT'17](http://link.springer.com/10.1007/978-3-319-70694-8_15) 232 | - [A Full RNS Variant of FV Like Somewhat Homomorphic Encryption Schemes, SAC'17](http://link.springer.com/10.1007/978-3-319-69453-5_23) 233 | - [Faster packed homomorphic operations and efficient circuit bootstrapping for TFHE, ASIACRYPT'17](10.1007/978-3-319-70694-8_14) 234 | - [Faster homomorphic linear transformations in HElib, CRYPTO'18](http://link.springer.com/10.1007/978-3-319-96884-1_4) 235 | - [Bootstrapping for Approximate Homomorphic Encryption, EUROCRYPT'18](https://link.springer.com/chapter/10.1007/978-3-319-78381-9_14) 236 | - [An Improved RNS Variant of the BFV Homomorphic Encryption Scheme, CT-RSA'19](http://link.springer.com/10.1007/978-3-030-12612-4_5) 237 | - [TFHE: Fast Fully Homomorphic Encryption Over the Torus, JOC'20](http://link.springer.com/10.1007/s00145-019-09319-x) 238 | - [Efficient Homomorphic Comparison Methods with Optimal Complexity, ASIACRYPT'20](https://link.springer.com/10.1007/978-3-030-64834-3_8) 239 | - [PEGASUS: Bridging polynomial and non-polynomial evaluations in homomorphic encryption, S&P'21](https://ieeexplore.ieee.org/document/9519408/) 240 | - [General Bootstrapping Approach for RLWE-based Homomorphic Encryption, ePrint'21](https://eprint.iacr.org/2021/691) 241 | - [On the Security of Homomorphic Encryption on Approximate Numbers, EUROCRYPT'21](https://link.springer.com/10.1007/978-3-030-77870-5_23) 242 | - [Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys, EUROCRYPT'21](https://link.springer.com/10.1007/978-3-030-77870-5_21) 243 | - [Efficient Homomorphic Conversion Between (Ring) LWE Ciphertexts, ACNS'21](https://link.springer.com/chapter/10.1007/978-3-030-78372-3_18) 244 | - [OpenFHE: Open-Source Fully Homomorphic Encryption Library, ePrint'22](https://eprint.iacr.org/2022/915) 245 | 246 | # 5 Differential Privacy (DP) 247 | ## 5.1 DP Papers 248 | 249 | - [Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias, JASA'65](https://www.jstor.org/stable/2283137?origin=crossref) 250 | - [Mechanism Design via Differential Privacy, FOCS'07](https://ieeexplore.ieee.org/document/4389483) 251 | - [How Much Is Enough? Choosing ε for Differential Privacy, ISC'11](http://link.springer.com/10.1007/978-3-642-24861-0_22) 252 | - [Differentially Private Empirical Risk Minimization, JMLR'11](https://www.jmlr.org/papers/volume12/chaudhuri11a/chaudhuri11a.pdf) 253 | - [Personal privacy vs population privacy, KDD'11](https://dl.acm.org/doi/10.1145/2020408.2020598) 254 | - [Functional Mechanism: Regression Analysis under Differential Privacy, VLDB'12](http://arxiv.org/abs/1208.0219) 255 | - [Stochastic gradient descent with differentially private updates, GlobalSIP'13](http://ieeexplore.ieee.org/document/6736861/) 256 | - [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response, CCS'14](http://arxiv.org/abs/1407.6981) 257 | - [Efficient Per-Example Gradient Computations, arXiv'15](http://arxiv.org/abs/1510.01799) 258 | - [Privacy-Preserving Deep Learning, CCS'15](https://dl.acm.org/doi/abs/10.1145/2810103.2813687) 259 | - [Concentrated Differential Privacy, arXiv'16](http://arxiv.org/abs/1603.01887) 260 | - [Deep Learning with Differential Privacy, CCS'16](http://arxiv.org/abs/1607.00133) 261 | - [Differentially Private Password Frequency Lists, NDSS'16](https://www.ndss-symposium.org/wp-content/uploads/2017/09/differentially-private-password-frequency-lists.pdf) 262 | - [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds, TCC'16](http://arxiv.org/abs/1605.02065) 263 | - [Rényi Differential Privacy, CSF'17](https://arxiv.org/abs/1702.07476) 264 | - [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, ICLR'17](http://arxiv.org/abs/1610.05755) 265 | - [Locally differentially private protocols for frequency estimation, USENIX Security'17](https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-wang-tianhao.pdf) 266 | - [Understanding the sparse vector technique for differential privacy, VLDB'17](https://arxiv.org/abs/1603.01699) 267 | - [Detecting Violations of Differential Privacy, CCS'18](http://arxiv.org/abs/1805.10277) 268 | - [Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting, CSF'18](https://arxiv.org/abs/1709.01604) 269 | - [Privacy Amplification by Iteration, FOCS'18](https://arxiv.org/abs/1808.06651) 270 | - [Learning Differentially Private Recurrent Language Models, ICLR'18](http://arxiv.org/abs/1710.06963) 271 | - [Scalable private learning with pate, ICLR'18](http://arxiv.org/abs/1802.08908) 272 | - [Differential Privacy: A Primer for a Non-Technical Audience, SSRN'18](https://www.ssrn.com/abstract=3338027) 273 | - [Rényi Differential Privacy of the Sampled Gaussian Mechanism, arXiv'19](http://arxiv.org/abs/1908.10530) 274 | - [That which we call private, arXiv'19](http://arxiv.org/abs/1908.03566) 275 | - [Differential Privacy in Practice: Expose your Epsilons!, JPC'19](https://journalprivacyconfidentiality.org/index.php/jpc/article/view/689) 276 | - [Understanding Gradient Clipping in Private SGD: A Geometric Perspective, NeurIPS'20](http://arxiv.org/abs/2006.15429) 277 | - [Locally Differentially Private Frequency Estimation with Consistency, NDSS'20](http://arxiv.org/abs/1905.08320) 278 | - [Automatic Discovery of Privacy–Utility Pareto Fronts, PETS'20](https://petsymposium.org/popets/2020/popets-2020-0060.php) 279 | - [Differential Privacy in the Shuffle Model: A Survey of Separations, arXiv'21](http://arxiv.org/abs/2107.11839) 280 | - [Tempered Sigmoid Activations for Deep Learning with Differential Privacy, AAAI'21](https://ojs.aaai.org/index.php/AAAI/article/view/17123) 281 | - [Differentially Private Learning Needs Better Features (or Much More Data), ICLR'21](http://arxiv.org/abs/2011.11660) 282 | - [Differentially Private Learning with Adaptive Clipping, NeurIPS'21](http://arxiv.org/abs/1905.03871) 283 | - [Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization, NeurIPS'21](http://arxiv.org/abs/2010.09063) 284 | - [Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping, PETS'21](https://petsymposium.org/popets/2021/popets-2021-0008.php) 285 | - [Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger, arXiv'22](http://arxiv.org/abs/2206.07136) 286 | - [Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy, NeurIPS'22](http://arxiv.org/abs/2205.10683) 287 | 288 | ## 5.2 DP Books 289 | 290 | - [The Algorithmic Foundations of Differential Privacy](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf), by Cynthia Dwork and Aaron Roth 291 | - [The Complexity of Differential Privacy](https://privacytools.seas.harvard.edu/files/privacytools/files/complexityprivacy_1.pdf), by Salil Vadhan 292 | - [Differential Privacy: From Theory to Practice](https://www.morganclaypool.com/doi/abs/10.2200/S00735ED1V01Y201609SPT018), by Ninghui Li, Min Lyu, Dong Su, Weining Yang 293 | 294 | ## 5.3 DP Courses 295 | 296 | - [Algorithms for Private Data Analysis](http://www.gautamkamath.com/CS860-fa2020.html), taught by Gautam Kamath, [Youtube](https://www.youtube.com/playlist?list=PLmd_zeMNzSvRRNpoEWkVo6QY_6rR3SHjp), [Bilibili](https://www.bilibili.com/video/av843424106/) 297 | - [Privacy in Statistics and Machine Learning](https://dpcourse.github.io), taught by Adam Smith and Jonathan Ullman 298 | 299 | ## 5.4 DP Libraries 300 | 301 | - [TensorFlow Privacy](https://github.com/tensorflow/privacy) - Training TensorFlow models with differential privacy 302 | - [Opacus](https://github.com/pytorch/opacus) - Training PyTorch models with differential privacy 303 | - [Google DP Library](https://github.com/google/differential-privacy) - Google's differential privacy libraries 304 | - [IBM DP Library](https://github.com/IBM/differential-privacy-library) - IBM's differential privacy library 305 | - [PyDP](https://github.com/OpenMined/PyDP) - OpenMined's Python DP library built on top of Google's 306 | - [PipelineDP](https://github.com/OpenMined/PipelineDP) - OpenMined's library for applying DP aggregations to large datasets 307 | - [OpenDP](https://github.com/opendp) - A modular collection of algorithms for building privacy-preserving applications 308 | 309 | # 6 Zero-Knowledge Proof (ZKP) 310 | - [简洁非交互零知识证明综述, 密码学报'22](http://www.jcr.cacrnet.org.cn/CN/Y2022/V9/I3/379) 311 | - [zk mooc](https://zk-learning.org/) 312 | 313 | # 7 Privacy-Preserving Machine Learning (PPML) 314 | 315 | ## 7.1 Papers 316 | * [Machine Learning Classification over Encrypted Data, NDSS'14](https://eprint.iacr.org/2014/331.pdf) 317 | * [Oblivious Multi-Party Machine Learning on Trusted Processors, USENIX SECURITY'16](https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/ohrimenko) 318 | * [CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, ICML'16](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/CryptonetsTechReport.pdf) 319 | * [CryptoDL: Deep Neural Networks over Encrypted Data, arXiv'17](https://arxiv.org/abs/1711.05189) 320 | * [Prio: Private, Robust, and Scalable Computation of Aggregate Statistics, NSDI'17](https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/corrigan-gibbs) 321 | * [SecureML: A System for Scalable Privacy-Preserving Machine Learning, S&P'17](https://eprint.iacr.org/2017/396) 322 | * [MiniONN: Oblivious Neural Network Predictions via MiniONN Transformations, CCS'17](https://acmccs.github.io/papers/p619-liuA.pdf) 323 | * [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, AsiaCCS'17](https://eprint.iacr.org/2017/1164) 324 | * [DeepSecure: Scalable Provably-Secure Deep Learning, DAC'17](https://arxiv.org/abs/1705.08963) 325 | * [Secure Computation for Machine Learning With SPDZ, NIPS'18](https://arxiv.org/abs/1901.00329) 326 | * [PySyft: A Generic Framework for Privacy Preserving Deep Learning, arXiv'18](https://arxiv.org/abs/1811.04017) 327 | * [ABY3: A Mixed protocol Framework for Machine Learning, CCS'18](https://eprint.iacr.org/2018/403.pdf) 328 | * [SecureNN: Efficient and Private Neural Network Training, PoPETs'18](https://eprint.iacr.org/2018/442.pdf) 329 | * [Gazelle: A Low Latency Framework for Secure Neural Network Inference, USENIX SECURITY'18](https://arxiv.org/abs/1801.05507) 330 | * [Private Machine Learning in TensorFlow using Secure Computation, arXiv'18](https://arxiv.org/abs/1810.08130) 331 | * [CHET: an optimizing compiler for fully-homomorphic neural-network inferencing, PLDI'19](https://dl.acm.org/citation.cfm?id=3314628) 332 | * [New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning, S&P'19](https://eprint.iacr.org/2019/599.pdf) 333 | * [Helen: Maliciously Secure Coopetitive Learning for Linear Models, S&P'19](https://ieeexplore.ieee.org/abstract/document/8835215) 334 | * [Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. CCS'19](https://dl.acm.org/citation.cfm?id=3363207) 335 | * [XONN: XNOR-based Oblivious Deep Neural Network Inference, USENIX Security'19](https://www.usenix.org/conference/usenixsecurity19/presentation/riazi) 336 | * [QUOTIENT: two-party secure neural network training and prediction, CCS'19](https://dl.acm.org/citation.cfm?id=3339819) 337 | * [ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction, CCSW'19](https://eprint.iacr.org/2019/429) 338 | * [SoK: Modular and Efficient Private Decision Tree Evaluation, PoPETs'19](https://eprint.iacr.org/2018/1099.pdf) 339 | * [Garbled Neural Networks are Practical, IACR ePrint'19](https://eprint.iacr.org/2019/338.pdf) 340 | * [Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, NDSS'20](https://eprint.iacr.org/2019/1315) 341 | * [BLAZE: Blazing Fast Privacy-Preserving Machine Learning, NDSS'20](https://eprint.iacr.org/2020/042) 342 | * [FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning, PoPETs'20](https://eprint.iacr.org/2019/1365) 343 | * [Secure Evaluation of Quantized Neural Networks, PoPETs'20](https://content.sciendo.com/view/journals/popets/2020/4/article-p355.xml) 344 | * [Delphi: A Cryptographic Inference Service for Neural Networks, USENIX SECURITY'20](https://eprint.iacr.org/2020/050) 345 | * [MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference, ARES'20](https://dl.acm.org/doi/abs/10.1145/3407023.3407045) 346 | * [SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search, USENIX Security'20](https://www.usenix.org/conference/usenixsecurity20/presentation/chen-hao) 347 | * [CrypTen: Secure multi-party computation meets machine learning, NeurIPS'20](https://arxiv.org/abs/2109.00984) 348 | * [An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference, ESORICS'20](https://link.springer.com/chapter/10.1007/978-3-030-58951-6_21) 349 | * [Secure and Verifiable Inference in Deep Neural Networks, ACSAC'20](https://dl.acm.org/doi/abs/10.1145/3427228.3427232) 350 | * [Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data, NeurIPS'20](https://arxiv.org/pdf/1911.07101.pdf) 351 | * [CrypTFlow: Secure TensorFlow Inference, S&P'20](https://eprint.iacr.org/2019/1049.pdf) 352 | * [CrypTFlow2: Practical 2-Party Secure Inference, CCS'20](https://arxiv.org/abs/2010.06457) 353 | * [ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing, arXiv'20](https://arxiv.org/abs/2006.04593) 354 | * [Practical Privacy-Preserving K-means Clustering, PoPETs'20](https://content.sciendo.com/view/journals/popets/2020/4/article-p414.xml) 355 | * [ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (Full Version), USENIX Security'21](https://eprint.iacr.org/2020/1225.pdf) 356 | * [SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning, USENIX Security'21](https://arxiv.org/abs/2005.10296) 357 | * [Privacy-preserving Density-based Clustering, AisaCCS'21](https://www.eurecom.fr/publication/6475/download/sec-publi-6475.0.pdf) 358 | * [SIRNN: A Math Library for Secure RNN Inference, S&P'21](https://eprint.iacr.org/2021/459) 359 | * [Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation, NDSS'21](https://www.ndss-symposium.org/ndss-paper/lets-stride-blindfolded-in-a-forest-sublinear-multi-client-decision-trees-evaluation/) 360 | * [MUSE: Secure Inference Resilient to Malicious Clients, USENIX Security'21](https://people.eecs.berkeley.edu/~raluca/MUSEcamera.pdf) 361 | * [DeepReDuce: ReLU Reduction for Fast Private Inference, USENIX Security'21](https://arxiv.org/abs/2103.01396) 362 | * [GForce: GPU-Friendly Oblivious and Rapid Neural Network Inference, USENIX Security'21](https://www.usenix.org/conference/usenixsecurity21/presentation/ng) 363 | * [CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU, S&P'21](http://arxiv.org/abs/2104.10949) 364 | * [GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks, NDSS'21](https://www.ndss-symposium.org/ndss-paper/gala-greedy-computation-for-linear-algebra-in-privacy-preserved-neural-networks/) 365 | * [Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security, USENIX Security'21](https://www.usenix.org/system/files/sec21fall-dalskov.pdf) 366 | * [When homomorphic encryption marries secret sharing: secure large-scale sparse logistic regression and applications in risk control, KDD'21](https://arxiv.org/abs/2008.08753) 367 | * [Circa: Stochastic ReLUs for Private Deep Learning, NeurIPS'21](https://proceedings.neurips.cc/paper/2021/file/11eba2991cc62daa4a85be5c0cfdae97-Paper.pdf) 368 | * [Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning, USENIX Security'21](https://eprint.iacr.org/2021/730) 369 | * [FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning, PoPETs'21](https://arxiv.org/abs/2004.02229) 370 | * [SoK: Efficient Privacy-preserving Clustering, PoPETs'21](https://eprint.iacr.org/2021/809) 371 | * [ZEN: Efficient Zero-Knowledge Proofs for Neural Networks, IACR ePrint'21](https://eprint.iacr.org/2021/087) 372 | * [zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy, CCS'21](https://eprint.iacr.org/2021/673) 373 | * [Adam in Private : Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation, arXiv'21](https://arxiv.org/abs/2106.02203) 374 | * [Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning, USENIX Security'21](https://www.usenix.org/conference/usenixsecurity21/presentation/zheng) 375 | * [Tetrad: Actively Secure 4PC for Secure Training and Inference, NDSS'22](https://arxiv.org/abs/2106.02850) 376 | * [SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/chandran) 377 | * [SIMC 2.0: Improved Secure ML Inference Against Malicious Clients, arXiv'22](https://arxiv.org/abs/2207.04637) 378 | * [Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference, USENIX Security'22](https://eprint.iacr.org/2022/207) 379 | * [Secure Poisson Regression, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/kelkar) 380 | * [SecFloat: Accurate Floating-Point meets Secure 2-Party Computation, S&P'22](https://eprint.iacr.org/2022/322) 381 | * [MPClan: Protocol Suite for Privacy-Conscious Computations, IACR ePrint'22](https://eprint.iacr.org/2022/675) 382 | * [LLAMA: A Low Latency Math Library for Secure Inference, PoPETs'22](https://eprint.iacr.org/2022/793) 383 | * [Pika: Secure Computation using Function Secret Sharing over Rings, PoPETs'22](https://eprint.iacr.org/2022/826) 384 | * [Piranha: A GPU platform for secure computation, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/watson) 385 | * [Secure Quantized Training for Deep Learning, ICML'22](https://arxiv.org/abs/2107.00501) 386 | * [Prio+: Privacy Preserving Aggregate Statistics via Boolean Shares, ePrint'22](https://eprint.iacr.org/2021/576) 387 | 388 | ## 7.2 Survey 389 | 390 | * [机器学习隐私保护研究综述, 软件学报'20](http://www.jos.org.cn/jos/article/abstract/6052) 391 | * [安全多方计算及其在机器学习中的应用, 计算机研究与发展'21](https://crad.ict.ac.cn/CN/10.7544/issn1000-1239.2021.20210626) 392 | 393 | ## 7.3 Videos 394 | * [Microsoft Research](https://www.youtube.com/playlist?list=PLD7HFcN7LXRef-eTSGt_XOUJLZNoDINUn). Videos from SEAL/CKKS talks at Microsoft's Private AI Bootcamp. 395 | -------------------------------------------------------------------------------- /abydevguide.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/primihub/Awesome-Privacy-Computing/2ed8fbd60d86803efa4ba8309149ce1188b403ed/abydevguide.pdf --------------------------------------------------------------------------------