damesca/mpc4j-mod

mpc4j

Introduction

Multi-Party Computation for Java (mpc4j) is an efficient and easy-to-use Secure Multi-Party Computation (MPC) library mainly written in Java.

The aim of mpc4j is to provide an academic library for researchers to study and develop MPC and related protocols in a unified manner. As mpc4j tries to provide state-of-the-art MPC implementations, researchers could leverage the library to have fair and quick comparisons between the new protocols they proposed and existing ones.

Contact

mpc4j is mainly developed by Weiran Liu. Feel free to contact me at mailto:[email protected]. The submodules involving Fully Homomorphic Encryption (FHE) is mainly developed by Liqiang Peng. The submodules involving Vector Oblivious Linear Evaluation (VOLE) is mainly developed by Hanwen Feng.

Who Uses mpc4j

Currently, DataTrust is powered by mpc4j. If your project uses mpc4j and you do not mind it appearing here please contact me.

Features

mpc4j has the following features:

• Aarch64 support: mpc4j can run on both x86_64 and aarch64. Researchers can develop and test protocols on Macbook M1 (aarch64) and then run experiments on Linux OS (x86_64).
• SM series support: In cases, developers may want to use SM series algorithms (SM2 for public-key operations, SM3 for hashing, and SM4 for block cipher operations) instead of regular algorithms (like secp256k1 for public key operations, SHA256 for hashing, and AES for block cipher operations). Also, the SM series algorithms are accepted by ISO/IES, so it may be necessary to support SM series algorithms under MPC settings. mpc4j leverages Bouncy Castle to support SM series algorithms.

Some Implementations of our Work

Package pmid in mpc4j-s2pc-pso contains the implementation of our paper "Efficient Private Multiset ID Protocols and Applications to Private Multiset Operations" (manuscript). The configuration files are under conf/pmid in mpc4j-s2pc-pso. We are still updating the paper. We will release the final version when possible.

Our paper "Linear Private Set Union from Multi-Query Reverse Private Membership Test" was accepted to USENIX Security 2023. Package psu in mpc4j-s2pc-pso contains the implementation of this paper. The configuration files are under conf/psu in mpc4j-s2pc-pso. Just run java -jar mpc4j-s2pc-pso-X.X.X-jar-with-dependencies.jar conf_file_name.txt separately on two platforms with direct network connections (using the network channel assigned in config files) or on two terminals in one platform (using local network 127.0.0.1).

Our paper "OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization" was accpeted to VLDB 2023. Module mpc4j-sml-opboost contains the implementation of this paper. The configuration files are under conf in mpc4j-sml-opboost. Like psu, just run java -jar mpc4j-s2pc-pso-X.X.X-jar-with-dependencies.jar conf_file_name.txt separately on two platforms with direct network connections (using the network channel assigned in config files) or on two terminals in one platform (using local network 127.0.0.1).

References

mpc4j includes some implementation ideas and codes from the following open-source libraries.

• smile: A fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. We understand many details of how to implement machine learning tasks from this library. We also introduce some codes into mpc4j for the dataset management and our privacy-preserving federated GBDT implementation. See packages edu.alibaba.mpc4j.common.data in mpc4j-common-data and package edu.alibaba.mpc4j.sml.smile in mpc4j-sml-opboost for details. Note that we introduce source codes that are released only under the GNU Lesser General Public License v3.0 (LGPLv3).
• Javallier: A Java library for Paillier partially homomorphic encryption based on python-paillier, with modifications to additionally support other schemes and optimizations. See mpc4j-crypto-phe for details.
• JNA GMP project: A JNA wrapper around the GNU Multiple Precision Arithmetic Library. We modify the code for supporting the aarch64 system. See mpc4j-common-jna-gmp for details.
• Bouncy Castle: A Java implementation of cryptographic algorithms, developed by the Legion of the Bouncy Castle, a registered Australian Charity. We understand many details of how to efficiently implement cryptographic algorithms using Java. We introduce its X25519 and Ed25519 implementations in mpc4j to support efficient Elliptic Curve Cryptographic (ECC) operations. See package edu.alibaba.mpc4j.common.tool.crypto.ecc.bc in mpc4j-common-tool for details.
• Rings: An efficient, lightweight library for commutative algebra. We understand how to efficiently do algebra operations from this library. We wrap its polynomial interpolation implementations in mpc4j. See package edu.alibaba.mpc4j.common.tool.polynomial in mpc4j-common-tool for details. We also provide JdkIntegersZp that uses JNA GMP to implement operations in $\mathbb{Z}_p$. See JdkIntegersZp in mpc4j-common-tool for details.
• mobile_psi_cpp: A C++ library implementing several OPRF protocols and using them for Private Set Intersection. We introduce its LowMC parameters and encryption implementations in mpc4j. See edu.alibaba.mpc4j.common.tool.crypto.prp.JdkBytesLowMcPrp and edu.alibaba.mpc4j.common.tool.crypto.prp.JdkLongsLowMcPrp in mpc4j-common-tool for details.
• blake2: Faster cryptographic hash function implementations. We introduce its original implementations and compare the efficiency with Java counterparts provided by Bouncy Castle and other hash functions (e.g., blake3). See crypto/blake2 in mpc4j-native-tool for details.
• blake3: Much faster cryptographic hash function implementations. We introduce its original implementations and compare the efficiency with Java counterparts provided by Bouncy Castle and other hash functions (e.g., blake2). See crypto/blake3 in mpc4j-native-tool for details.
• emp-toolkit: Efficient bit-matrix transpose (See bit_matrix_trans in mpc4j-native-tool), AES-NI implementations (See crypto/aes.h in mpc4j-native-tool), efficient $GF(2^\kappa)$ operations (See gf2k in mpc4j-native-tool), and the implementation of the Silent OT protocol presented in the paper "Ferret : Fast Extension for coRRElated oT with Small Communication" accepted at CCS 2020 (See cot in mpc4j-s2pc-pcg).
• Kunlun: A C++ wrapper for OpenSSL, making it handy to use without worrying about cumbersome memory management and memorizing complex interfaces. Based on this wrapper, Kunlun builds an efficient and modular crypto library. We introduce its OpenSSL wrapper for Elliptic Curve and the Window Method implementation in mpc4j, see ecc_openssl in mpc4j-native-tool for details.
• KyberJCE: Kyber is an IND-CCA2-secure key encapsulation mechanism (KEM), whose security is based on the hardness of solving the learning-with-errors (LWE) problem over module lattices. KyberJCE is a pure-Java implementation of Kyber. We introduce its Kyber implemention in mpc4j for supporting post-quantum secure oblivious transfer. See crypto/kyber in mpc4j-native-tool for details.
• PSI-analytics: The implementation of the protocols presented in the paper "Private Set Operations from Oblivious Switching" accepted at PKC 2021. We introduce its switching network implementations in mpc4j. See package benes_network in mpc4j-native-tool for details.
• Diffprivlib: A general-purpose library for experimenting with, investigating, and developing applications in differential privacy. We understand how to organize source codes for implementing differential privacy mechanisms. See mpc4j-dp-cdp for details.
• b2_exponential_mchanism: An exponential mechanism implementation with base-2 differential privacy. We re-implement the base-2 exponential mechanism in mpc4j. See package edu.alibaba.mpc4j.dp.cdp.nomial for details.
• libOTe: Implementations for many Oblivious Transfer (OT) protocols, especially the Silent OT protocol presented in the paper "Silver: Silent VOLE and Oblivious Transfer from Hardness of Decoding Structured LDPC Codes" accepted at CRYPTO 2021 (See package cot in mpc4j-s2pc-pcg).
• PSU: The implementation of the paper "Scalable Private Set Union from Symmetric-Key Techniques," published in ASIACRYPT 2019. We introduce its fast polynomial interpolation implementations in mpc4j. See package ntl_poly in mpc4j-native-tool for details. The PSU implementation is in package psu of mpc4j-s2pc-pso.
• PSU: The implementation of the paper "Shuffle-based Private Set Union: Faster and More," published in USENIX Security 2022. We introduce the idea of how to concurrently run the Oblivious Switching Network (OSN) in mpc4j. See package psu in mpc4j-s2pc-pso for details.
• SpOT-PSI: The implementation of the paper "SpOT-Light: Lightweight Private Set Intersection from Sparse OT Extension," published in CRYPTO 2019. We introduce many ideas for fast polynomial interpolations in mpc4j. The source code is not merged currently.
• OPRF-PSI: The implementation of the paper "Private Set Intersection in the Internet Setting From Lightweight Oblivious PRF," published in CRYPTO 2020. We introduce its OPRF implementations in mpc4j. See oprf in mpc4j-s2pc-pso for details.
• APSI: The implementation of the paper "Labeled PSI from Homomorphic Encryption with Reduced Computation and Communication," published in CCS 2021. For its source code, we understand how to use the Fully Homomorphic Encryption (FHE) library SEAL. Most of the codes for Unbalanced Private Set Intersection (UPSI) are partially from ASPI. We also adapt the encoding part of 6857-private-categorization to support arbitrary bit-length elements. See mpc4j-native-fhe and upsi in mpc-s2pc-pso for details.
• xgboost-predictor: Pure Java implementation of XGBoost predictor for online prediction tasks. This work is released under the Apache Public License 2.0. We understand the format of the XGBoost model from this library. We also introduce some codes in mpc4j for our privacy-preserving federated XGBoost implementation. See packages ai.h2o.algos.tree and biz.k11i.xgboost in mpc4j-sml-opboost for details.

Acknowledge

We thank Prof. Benny Pinkas and Dr. Avishay Yanai for many discussions on the implementation of Private Set Intersection protocols. They also bring much help to our Java implementations for Oblivious Key-Value Storage (OKVS) presented in the paper "Oblivious Key-Value Stores and Amplification for Private Set Intersection," accepted at CRYPTO 2021. See package okve/okvs in mpc4j-common-tool for more details.

We thank Dr. Stanislav Poslavsky and Prof. Benny Pinkas for many discussions on implementations of fast polynomial interpolations when we try to implement the PSI protocol presented in the paper "SpOT-Light: Lightweight Private Set Intersection from Sparse OT Extension."

We thank Prof. Mike Rosulek for the discussions about the implementation of Private Set Union (PSU). Their implementation for the paper "Private Set Operations from Oblivious Switching" brings much help for us to understand how to implement PSU.

We thank Prof. Xiao Wang for discussions about fast bit-matrix transpose. From the discussion, we understand that the basic idea of fast bit-matrix transpose is from the blog The Full SSE2 Bit Matrix Transpose Routine. He also helped me realize that there exists an efficient polynomial operation implementation in $GF(2^\kappa)$ introduced in Intel Carry-Less Multiplication Instruction and its Usage for Computing the GCM Mode. See package galoisfield/gf2k in mpc4j-common-tool for more details.

We thank Prof. Peihan Miao for discussions about the implementation of the paper "Private Set Intersection in the Internet Setting From Lightweight Oblivious PRF." From the discussion, we understand there is a special case for the lightweight OPRF when $n = 1$. See package oprf in mpc4j-s2pc-pso for more details.

We thank Prof. Yu Chen for many discussions on various MPC protocols. Here we recommend his open-source library Kunlun, a modern crypto library. We thank Minglang Dong for her example codes about implementing the Window Method for fixed-base multiplication in ECC.

We thank Dr. Bolin Ding for many discussions on how to introduce MPC into the database field. Here we recommend the open-source library FederatedScope, an easy-to-use federated learning package, from his team.

License

This library is licensed under the Apache License 2.0.

Specifications

Most of the codes are in Java, except for very efficient implementations in C/C++. You need OpenSSL, GMP, NTL,, MCL and libsodium to compile mpc4j-native-tool, and SEAL 4.0.0 to compile mpc4j-native-fhe. Please see READMD.md in mpc4j-native-cool on how to install required C/C++ libraries.

After successfully obtaining the compiled C/C++ library (named libmpc4j-native-tool and libmpc4j-native-fhe, respectively), you need to assign the native library location when running mpc4j using -Djava.library.path.

Tests

mpc4j has been tested on MAC OS x86_64, MAC OS M1, and Linux x86_64. We welcome developers to do tests on other platforms.

Development

Development Guideline

We develop mpc4j using Intellij IDEA and CLion. After successfully compiling mpc4j-native-tool and mpc4j-native-fhe (Please see the documentation in these modules for more details on how to compile them), you need to configure IDEA with the following procedures so that IDEA can link to these native libraries.

1. Open Run->Edit Configurations...
2. Open Edit Configuration templates...
3. Select JUnit.
4. Add the following command into VM Options:

-Djava.library.path=/YOUR_MPC4J_ABSOLUTE_PATH/mpc4j-native-tool/cmake-build-release:/YOUR_MPC4J_ABSOLUTE_PATH/mpc4j-native-fhe/cmake-build-release

Demonstration

We thank Qixian Zhou for writing a guideline with a demonstration on how to config the development environment on macOS (x86_64). We believe this guideline can also be used for other platforms, e.g., macOS (M1), Ubuntu, and CentOS. Here are the steps:

1. Follow any guidelines to install JDK 8 and IntelliJ IDEA. If you successfully install JDK8, you can obtain similar information in the terminal when executing java -version.

java version "1.8.0_301"
Java(TM) SE Runtime Environment (build 1.9.0_301-b09)
Java HotSpot(TM) 64-Bit Server VM (build 25.301-b09, mixed mode)

2. Clone mpc4j source code using git clone https://github.com/alibaba-edu/mpc4j.git.

3. Follow the documentation in https://github.com/alibaba-edu/mpc4j/tree/main/mpc4j-native-tool to compile mpc4j-native-tool. If all steps are correct, you will see:

[100%] Linking CXX shared library libmpc4j-native-tool.dylib
[100%] Built target mc4j-native-tool

4. Follow the documentation in https://github.com/alibaba-edu/mpc4j/tree/main/mpc4j-native-fhe to compile mpc4j-native-tool. If all steps are correct, you will see:

[100%] Linking CXX shared library libmpc4j-native-fhe.dylib
[100%] Built target mc4j-native-fhe

5. Using IntelliJ IDEA to open mpc4j.
6. Open Run->Edit Configurations....

5. Open Edit Configuration templates....

5. Select JUnit, and add the following command into VM Options (Note that you must replace /YOUR_MPC4J_ABSOLUTE_PATH with your own absolute path for libmpc4j-native-tool.dylib and libmpc4j-native-fhe.dylib.):

-Djava.library.path=/YOUR_MPC4J_ABSOLUTE_PATH/mpc4j-native-tool/cmake-build-release:/YOUR_MPC4J_ABSOLUTE_PATH/mpc4j-native-fhe/cmake-build-release

9. Now, you can run tests of any submodule by pressing the Green Arrows showing on the left of the source code in test packages.

TODO List

Possible Missions

• Provide more documentation.
• Translate JavaDoc and comments in English.
• We are still adjusting our implementations on many Private Set Intersection protocols. We will soonly release the source code whenever available.
• More secure two-party computation (2PC) protocol implementations.
• More secure three-party computation (3PC) protocol implementations. Specifically, release the source code of our paper "Scape: Scalable Collaborative Analytics System on Private Database with Malicious Security" accepted at ICDE 2022.
• More differentially private algorithms and protocols, especially for the Shuffle Model implementations of our paper "Privacy Enhancement via Dummy Points in the Shuffle Model."

Impossible Missions, but We Will Try

• We have unified ECC operations provided by MCL, OpenSSL, and Bouncy Castle. We decided not to include Relic since Relic needs to decide its underlying elliptic curve when compiling. We hope to include libsodium into mpc4j so that we can finally have a unified ECC operation interface and can compare their efficiencies on the same platform.
• Currently, we are trying to introduce LWE-based schemes in mpc4j, including CRYSTALS KYBER Java and its C/C++ counterpart. If so, mpc4j would have schemes and protocols with post-quantum security.
• What about implementing "Deep Learning with Differential Privacy" and its following works using Java, e.g., based on Deep Java Library?
• (Suggested by Prof. Joe Near) What about implementing Distributed Noise Generation protocols, like "Our Data, Ourselves: Privacy via Distributed Noise Generation"?