Tenseal

The convolution operation introduced in this alpha release have an experimental API that should be updated in upcoming releases.

New features
- Serialization of TenSEALContext and tensor types (101, 109)
- Threadpool support and parallel vector-matrix multiplication (115, 124)
- Check if a key is found in a TenSEALContext (132)
- Convolution using im2col technique (117, 134, 138)
- Encrypted matrix multiplication with plain vector (137, 138)
- Vector packing (141)

Bug fixes
- make_public_context should keep the existing keys (110)
- Better handling of transparent ct exceptions (119)
- Check for empty vector upon encryption (126)
- Fix CKKS and BFV vectors constructors (133)

Other
- Tutorial 1 - Training and Evaluation of Logistic Regression on Encrypted Data (85)
- Update docker images, docs and tags (104, 111, 114)
- Upgrade SEAL to 3.5.6 (105)
- Bazel build (116)
- Automatic tests on [Linux, MacOS, Windows] x [py36, py37, py38] (128)
- Code optimization (142)
- Fix release workflow (143)

- Scalar operations for CKKSVector (86)
- Operations on vectors of different size (1 vs n) (92)
- Negate and right arithmetic operators for CKKSVector (93, 94)
- Encrypted vector exponentiation and polynomial evaluation with optimum circuit (99, 100)

With all this recent changes, TenSEAL is now able to perform training of a logistic regression model, checkout the tutorial section for more information.

- Fixing package build for MacOS (87 ) and Windows (89)

This is the first official release of TenSEAL, so we will mainly focus on what this first version have been building so far

- Encryption/Decryption of vectors of real numbers or integers using either CKKS or BFV respectively
- Element-wise addition, substraction and multiplication of encrypted-encrypted vectors and encrypted-plain vectors
- Dot product and vector-matrix multiplication
- Complete SEAL API under `tenseal.sealapi`
- Support for Linux, Windows and MacOS
- Support for Python 3.6, 3.7 and 3.8
- Docker images with TenSEAL for Python 3.6, 3.7 and 3.8
- Tutorial for [Getting Started](https://github.com/OpenMined/TenSEAL/blob/master/tutorials%2FTutorial%200%20-%20Getting%20Started.ipynb)
- Automation of build, release and tests of both Python and C++ code.

Thanks to all the [contributors](https://github.com/OpenMined/TenSEAL/graphs/contributors) of the TenSEAL library, more specifically to philomath213 and bcebere for their time and efforts spent to be able able to make this first release.