Create a Calculation View with Differential Privacy

Create a new folder called models under db/src. Create a new Calculation View and call it SALARIES_ANONYMIZED

Click on the anonymization node and then click on the white canvas to drop it.

Use the on the node to add a table as a data source. Choose TGT_SALARIES

Double click on the Anonymize_1 node. This will open the mapping. Double click on TGT_SALARIES to add all of the columns to the output

Click Details and switch k-anonymity to Differential Privacy

Configure the following parameters for anonymity

• Sequence Column: ID
• Epsilon: 0.1
• Sensitivity: 15000
• Noised Column: SALARY

For example:

For more information about these parameters check the SAP HANA Security Guide

Connect the Anonymize_1 node to the Aggregation node.

Double-click on the name of the node to move all the fields into the output columns.

Go into the Semantics node and switch START_YEAR to attribute

Save and Build the calculation view

Right-click on it and choose Data Preview

Click Raw Data and you will see the anonymized data:

See the negative salaries? The added noise guarantees privacy while keeping sum and average results similar to the original dataset.

Use the Analysis tab and drag the GENDER and REGION fields to the label axis and ID to the Value axis.
Change the aggregation for SALARY to COUNT to answer the question below.

If you have a tenant in SAP Analytics Cloud or a reporting tool such as Analysis for Office, you can configure the SAP HANA Analytics Adapter. The adapter is a Java application that exposes Calculation Views for consumption through Information Access (or InA).

Follow the steps in the following blog post if you want to set up the HANA Analytics Adapter: (https://blogs.sap.com/2019/04/24/connecting-the-sap-hana-service-on-cloud-foundry-to-sap-analytics-cloud-the-lazy-approach-pt1/)[https://blogs.sap.com/2019/04/24/connecting-the-sap-hana-service-on-cloud-foundry-to-sap-analytics-cloud-the-lazy-approach-pt1/].