SAP HANA Anonymized Views
The concept name was not provided (undefined), so a precise definition cannot be written. Based on the available sources — which cover data anonymization techniques in SAP HANA Cloud (such as K-Anonymity, L-Diversity, and Differential Privacy), anonymized views, and Calculation Views — the concept could relate to any of these topics. Please supply a valid concept name so that a grounded, accurate definition can be produced.
Tutorials that teach this
- Tutorial Create a Calculation View of Type Cube in the SAP HANA Cloud
- Tutorial Understand How Data Anonymization Works in SAP HANA Cloud, SAP HANA Database
- Tutorial Create an Anonymized View Using K-Anonymity
- Tutorial Monitor and Share Anonymized Data
- Tutorial Create Anonymized Views Using Differential Privacy
- Tutorial Create an Anonymized View Using L-Diversity
- Tutorial Understand Why Data Anonymization is Important
Prerequisites
- Concept L-Diversity for Sensitive Data Protection Data anonymization in SAP HANA Cloud is a technique that protects sensitive personal data by transforming it so that individuals cannot be identified, while still allowing the data to be used for analysis. Developers use it to create anonymized views — for example, with methods such as [L-Diversity](https://developers.sap.com/tutorials/hana-cloud-data-anonymization-3.html) — that expose only privacy-safe data to consumers. These views can then be [monitored and shared](https://developers.sap.com/tutorials/hana-cloud-data-anonymization-4.html) to ensure ongoing compliance and controlled data access.
- Concept Data Privacy and Anonymization Techniques A technique used in SAP HANA Cloud, SAP HANA Database to protect sensitive personal data by transforming it so that individuals cannot be identified from a dataset. Developers use it to create **anonymized views** — using methods such as [K-Anonymity, L-Diversity, or Differential Privacy](https://developers.sap.com) — that allow data to be shared and analyzed while reducing the risk of re-identification. These anonymized views can then be monitored and shared with consumers who need access to the data without exposing personally identifiable information.
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