Package: tenseal
TenSEALA library for doing homomorphic encryption operations on tensorsTenSEAL is a library for doing homomorphic encryption operations on tensors, built on top ofMicrosoft SEAL. It provides ease of use through a Python API, while preserving efficiency by implementing most of its operations using C++.Features:key: Encryption/Decryption of vectors of integers using BFV:old_key: Encryption/Decryption of vectors of real numbers using CKKS:fire: Element-wise addition, subtraction and multiplication of encrypted-encrypted vectors and encrypted-plain vectors:cyclone: Dot product and vector-matrix multiplication:zap: Complete SEAL API undertenseal.sealapiUsageWe show the basic operations over encrypted data, more advanced usage for machine learning applications can be found on ourtutorial sectionimporttensealasts# Setup TenSEAL contextcontext=ts.context(ts.SCHEME_TYPE.CKKS,poly_modulus_degree=8192,coeff_mod_bit_sizes=[60,40,40,60])context.generate_galois_keys()context.global_scale=2**40v1=[0,1,2,3,4]v2=[4,3,2,1,0]# encrypted vectorsenc_v1=ts.ckks_vector(context,v1)enc_v2=ts.ckks_vector(context,v2)result=enc_v1+enc_v2result.decrypt()# ~ [4, 4, 4, 4, 4]result=enc_v1.dot(enc_v2)result.decrypt()# ~ [10]matrix=[[73,0.5,8],[81,-5,66],[-100,-78,-2],[0,9,17],[69,11,10],]result=enc_v1.matmul(matrix)result.decrypt()# ~ [157, -90, 153]InstallationUsing pip$pipinstalltensealThis installs the last packaged version onpypi. If your platform doesn't have a ready package, please open anissueto let us know.Build from SourceSupported platforms and their requirements are listed below: (this are only required for building TenSEAL from source)Linux:A modern version of GNU G++ (>= 6.0) or Clang++ (>= 5.0).MacOS:Xcode toolchain (>= 9.3)Windows:Microsoft Visual Studio (>= 10.0.40219.1, Visual Studio 2010 SP1 or later).If you want to install tenseal from the repository, you should first make sure to have the requirements for your platform (listed above) andCMake (3.14 or higher)installed, then get the third party libraries (if you didn't already) by running the following command from the root directory of the project$gitsubmoduleinit
$gitsubmoduleupdateTenSEAL usesProtocol Buffersfor serialization, and you will need the protocol buffer compiler too.If you are on Windows, you will first need to build SEAL library using Visual Studio, you should use the solution fileSEAL.slninthird_party/SEALto build the projectnative\src\SEAL.vcxprojwithConfiguration=ReleaseandPlatform=x64. For more details check the instructions inBuilding Microsoft SEALYou can then trigger the build and the installation$pipinstall.Use DockerYou can use ourDocker imagefor a ready to use environment with TenSEAL installed$dockercontainerrun--interactive--ttyopenmined/tensealNote:openmined/tensealpoints to the image from the last release, useopenmined/tenseal:devfor the image built from the master branch.You can also build your custom image, this might be handy for developers working on the project$dockerbuild-ttenseal-fdocker-images/Dockerfile-py38.To interactively run this docker image as a container after it has been built you can run$dockercontainerrun-ittensealUsing BazelTo use this library in another Bazel project, add the following in your WORKSPACE file:git_repository(
   name = "org_openmined_tenseal",
   remote = "https://github.com/OpenMined/TenSEAL",
   branch = "master",
   init_submodules = True,
)

load("@org_openmined_tenseal//tenseal:preload.bzl", "tenseal_preload")

tenseal_preload()

load("@org_openmined_tenseal//tenseal:deps.bzl", "tenseal_deps")

tenseal_deps()BenchmarksYou can benchmark the implementation at any point by running$bazelrun-copt--spawn_strategy=standalone//tests/cpp/benchmarks:benchmarkThe benchmarks from every PR merge are uploadedhere.TutorialsGetting StartedTutorial 1 - Training and Evaluation of Logistic Regression on Encrypted DataTutorial 2 - Working with Approximate NumbersTutorial 3 - BenchmarksTutorial 4 - Encrypted Convolution on MNISTPublicationsA. Benaissa, B. Retiat, B. Cebere, A.E. Belfedhal,"TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption", ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021).@misc{tenseal2021,
    title={TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption}, 
    author={Ayoub Benaissa and Bilal Retiat and Bogdan Cebere and Alaa Eddine Belfedhal},
    year={2021},
    eprint={2104.03152},
    archivePrefix={arXiv},
    primaryClass={cs.CR}
}SupportFor support in using this library, please join the#supportSlack channel.Click here to join our Slack community!ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseApache License 2.0