MathNet.Numerics.Data.Text 4.0.0-beta01

Text Data Input/Output Extensions for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use.

This is a prerelease version of MathNet.Numerics.Data.Text.

There is a newer version of this package available.
See the version list below for details.

Install-Package MathNet.Numerics.Data.Text -Version 4.0.0-beta01

dotnet add package MathNet.Numerics.Data.Text --version 4.0.0-beta01

<PackageReference Include="MathNet.Numerics.Data.Text" Version="4.0.0-beta01" />

For projects that support PackageReference, copy this XML node into the project file to reference the package.

paket add MathNet.Numerics.Data.Text --version 4.0.0-beta01

The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

Support for Math.NET Numerics v4
In addition to .Net 4.0 and newer now also targets .Net Standard 1.3 and 2.0.

Dependencies

.NETFramework 4.0
- MathNet.Numerics (>= 4.0.0-beta06)
.NETStandard 1.3
- MathNet.Numerics (>= 4.0.0-beta06)
.NETStandard 2.0
- MathNet.Numerics (>= 4.0.0-beta06)

Used By

NuGet packages (2)

Showing the top 2 NuGet packages that depend on MathNet.Numerics.Data.Text:

Package	Downloads
Octavo.NET Octavo.NET.Core Class Library	609
PhilipsTang.Mathlib PhilipsTang.Mathlib .NETStandard 2.0	206

GitHub repositories (1)

Showing the top 1 popular GitHub repositories that depend on MathNet.Numerics.Data.Text:

Repository	Stars
microsoft/CryptoNets CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.	135

Repository

Stars

microsoft/CryptoNets

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

135

Version History

Version	Downloads	Last updated
4.0.0	14,721	2/14/2018
4.0.0-beta01	482	2/4/2018
3.2.1	2,383	4/29/2017
3.2.0	3,538	4/11/2016
3.1.1	5,202	7/13/2015
3.1.0	1,470	1/11/2015
3.0.0	1,301	7/23/2014
3.0.0-beta02	551	6/15/2014
3.0.0-beta01	576	4/23/2014
3.0.0-alpha9	541	3/29/2014
3.0.0-alpha8	543	2/26/2014
3.0.0-alpha7	546	12/30/2013
3.0.0-alpha6	564	12/3/2013
3.0.0-alpha5	612	11/15/2013
3.0.0-alpha4	553	9/23/2013
1.1.0	1,459	6/23/2013