ldplearn

by wangsw Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | ldplearn Summary

ldplearn is a Python library. ldplearn has no bugs, it has no vulnerabilities and it has low support. However ldplearn build file is not available. You can download it from GitHub.

ldplearn

Support

Quality

Security

License

Reuse

Support

ldplearn has a low active ecosystem.

It has 1 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

ldplearn has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ldplearn is current.

Quality

ldplearn has no bugs reported.

Security

ldplearn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

ldplearn does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

ldplearn releases are not available. You will need to build from source code and install.

ldplearn has no build file. You will be need to create the build yourself to build the component from source.

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ldplearn Key Features

No Key Features are available at this moment for ldplearn.

ldplearn Examples and Code Snippets

No Code Snippets are available at this moment for ldplearn.

Community Discussions

No Community Discussions are available at this moment for ldplearn.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ldplearn

You can download it from GitHub.
You can use ldplearn like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

binary randomized response (brr)[3] for categorical data distribution estimation. multinomial randomzed response (mrr)[3] for categorical data distribution estimation. k-subset mechanism (k-subset)[3] for categorical data distribution estimation. binary randomized repsonse for set-valued data (brrset, RAPPOR) [1] distribution estimation. privset mechanism (privset)[2] for set-valued data distribution estimation. [1] Erlingsson, Úlfar, Vasyl Pihur, and Aleksandra Korolova. "Rappor: Randomized aggregatable privacy-preserving ordinal response." Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. ACM, 2014. [2] Wang Shaowei, Huang Liusheng, Nie Yiwen, Wang Pengzhan, Xu Hongli, Yang Wei. "PrivSet: Set-valued Data Analyses with Local Differential Privacy." The 37th Annual IEEE International Conference on Computer Communications (INFOCOM). IEEE, 2018. [3] Wang Shaowei, Huang Liusheng, Wang Pengzhan, Nie Yiwen, Xu Hongli, Yang Wei, Qiao, Chunming. "Mutual Information Optimally Local Private Discrete Distribution Estimation." arXiv preprint arXiv:1607.08025.

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