Using Sharemind for data mining

Why is privacy-preserving data mining important?

Organizations can provide better services with better data. However, this data is not always available due to privacy or confidentiality requirements. These problems could be solved (or the risks decreased) by using privacy-preserving data mining. Sharemind is designed for building data mining and data aggregation applications and is therefore suitable for such systems.

Shopping basket analysis with Sharemind

Shopping basket analysis is a data mining problem that tries to analyze peoples' shopping habits. Typically, the goal is to understand which products are often bought together. This information will then allow retail companies to make special offers, rearrange malls or review the product list.

We have demonstrated the feasibility of privacy-preserving data mining with Sharemind by implementing three shopping basket analysis algorithms in the SecreC programming language.

The experiments and results are best described in the master's thesis of Roman Jagomägis. You can find the PDF here.

Try it out yourself!

The code and example data for running these algorithms are packaged within SecreC SDK. After you have the SDK, see the tutorials on how to install it and run the example code.