tno.mpc.mpyc.statistics
TNO PET Lab - secure Multi-Party Computation (MPC) - MPyC - Statistics
Science Score: 44.0%
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Low similarity (10.1%) to scientific vocabulary
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Repository
TNO PET Lab - secure Multi-Party Computation (MPC) - MPyC - Statistics
Basic Info
- Host: GitHub
- Owner: TNO-MPC
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://docs.pet.tno.nl/mpc/mpyc/statistics
- Size: 14.6 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
TNO MPC Lab - MPyC - Statistics
The TNO MPC lab consists of generic software components, procedures, and functionalities developed and maintained on a regular basis to facilitate and aid in the development of MPC solutions. The lab is a cross-project initiative allowing us to integrate and reuse previously developed MPC functionalities to boost the development of new protocols and solutions.
The package tno.mpc.mpyc.statistics is part of the TNO Python Toolbox.
Within the LANCELOT project, a collaboration between TNO, IKNL and Janssen, TNO developed and implemented secure statistics. LANCELOT is partly funded by PPS-surcharge for Research and Innovation of the Dutch Ministry of Economic Affairs and Climate Policy. The Appl.AI project SELECTED, partly funded by NLAIC, also contributed to specific components in secure statistics (correlation, covariance).
Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.
This implementation of cryptographic software has not been audited. Use at your own risk.
Documentation
Documentation of the tno.mpc.mpyc.statistics package can be found here.
Install
Easily install the tno.mpc.mpyc.statistics package using pip:
console
$ python -m pip install tno.mpc.mpyc.statistics
If you wish to run the tests you can use:
console
$ python -m pip install 'tno.mpc.mpyc.statistics[tests]'
Note:
A significant performance improvement can be achieved by installing the GMPY2 library.
console
$ python -m pip install 'tno.mpc.mpyc.statistics[gmpy]'
Usage
The statistics module can be used as follows:
```python import numpy as np from mpyc.runtime import mpc from tno.mpc.mpyc.statistics import covariance
secnum = mpc.SecFxp(l=64, f=32)
def getmpcdata(row1, row2): row1mpc = [secnum(x) for x in row1] row2mpc = [secnum(y) for y in row2] return row1mpc, row2mpc
def distributedataoverplayers(row1mpc, row2mpc): row1mpcshared = mpc.input(row1mpc, senders=0) row2mpcshared = mpc.input(row2mpc, senders=0) return row1mpcshared, row2mpc_shared
async def covariance_example(): print("Covariance example")
row_1 = [1.0, 3.0, 2.0, 1.0, 5.0, 6.0, 3.0]
row_2 = [2.0, 11.0, 9.0, 0.0, 8.0, 2.0, 2.1]
row_1_np = np.array(row_1)
row_2_np = np.array(row_2)
row_1_mpc, row_2_mpc = get_mpc_data(row_1_np, row_2_np)
async with mpc:
row_1_mpc_shared, row_2_mpc_shared = distribute_data_over_players(
row_1_mpc, row_2_mpc
)
secure_cov = covariance(row_1_mpc_shared, row_2_mpc_shared)
revealed_cov = await mpc.output(secure_cov)
np_cov = np.cov(row_1, row_2)[0][1]
print("Secure Covariance: ", revealed_cov)
print("Numpy Covariance:", np_cov)
if name == "main": mpc.run(covariance_example()) ```
Owner
- Name: TNO - MPC Lab
- Login: TNO-MPC
- Kind: organization
- Email: mpclab@tno.nl
- Location: Anna van Buerenplein 1, 2595 DA Den Haag, The Netherlands
- Website: https://mpc.tno.nl
- Repositories: 19
- Profile: https://github.com/TNO-MPC
TNO - MPC Lab
Citation (CITATION.cff)
cff-version: 1.2.0
license: Apache-2.0
message: If you use this software, please cite it using these metadata.
authors:
- name: TNO MPC Lab
city: The Hague
country: NL
email: mpclab@tno.nl
website: https://mpc.tno.nl
type: software
url: https://mpc.tno.nl
contact:
- name: TNO MPC Lab
city: The Hague
country: NL
email: mpclab@tno.nl
website: https://mpc.tno.nl
repository-code: https://github.com/TNO-MPC/mpyc.statistics
repository-artifact: https://pypi.org/project/tno.mpc.mpyc.statistics
title: TNO MPC Lab - MPyC - Statistics
version: v0.1.1
date-released: 2022-05-18
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- Average comments per issue: 0
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Packages
- Total packages: 1
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Total downloads:
- pypi 19 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
pypi.org: tno.mpc.mpyc.statistics
Secure Data Exploratory Analyses on Vertically Partitioned Data
- Homepage: https://mpc.tno.nl/
- Documentation: https://docs.mpc.tno.nl/mpyc/statistics/0.1.1
- License: Apache License, Version 2.0
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Latest release: 0.1.1
published almost 4 years ago