Welcome to the crandas documentation!
Roseman Labs is software for secure data collaboration, based on multi-party computation (MPC). Crandas is a DataFrame library made to interact with the Roseman Labs cryptographic engine. With crandas, users can interact with data using MPC in a way familiar to data scientists without having to worry about the underlying cryptography.
Crandas can do the following:
- Data filtering and tabular manipulation
- Combination of different data sources
- Secure aggregations and other data analyses
Crandas also has an ever-growing library of privacy-preserving machine learning algorithms and statistical tools:
- Linear and logistic regression
- Random forests and k-nearest neighbors
- Hypothesis testing and other descriptive statistics
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If you have just started working with Roseman Labs, you will need to deploy the environment. Set up the environment and integrate it with your workflow.
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Build analyses, run machine learning algorithms and extract insights from your data through the
crandaspython package. -
Access SDKs, define rules for authorized analyses and build applications that protect data privacy.
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Find how the system works to ensure that you can find all the insights you need while your data is kept safe and secure.
If you are new to crandas, the Getting Started guide will walk you through the process of installation up to basic data manipulation. If you are familiar with other DataFrame libraries, such as pandas, you might want to read this article to find the essential differences.
For more in-depth information about functionalities, consult the User Guide. Additionally, you can go through some Tutorials to familiarize yourself with the different features present in crandas.