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Why?

In this guide, we use the YFCC100m dataset to demonstrate application of the LBSN Structure scheme, to illustrate possible privacy-conflicts, and to discuss approaches and best-practice examples to enhancing user-privacy in various contexts of visual analytics.

A basis for communcation

Our rationale is that only an open discussion and a clear communication can help improving user privacy on a broad basis. While many approaches to enhancing user privacy already exist, specific methods are sometimes difficult to adapt or follow.

With the examples provided in this guide, we attempt to cover all steps of processing. This allows us to illustrate and discuss approaches to privacy-protection in-depth and in a detailed fashion, particularly beyond what is possible in peer-reviewer papers.

Limitations apply

We emphasize that the examples presented here are not universally applicable. Privacy conflicts emerge from specific contexts of application, and it is not possible to declare rules that apply to all possible contexts.

Particularly, this guide provides no legal advice whatsoever. The demonstrations provided here can provide a basis for anyone voluntarily interested in enhancing data processing workflows.

Contributions welcome

We also invite anyone to contribute, either by enriching the discussion or by adding guides or improving existing ones. Please see the Developers Section.


Last update: June 18, 2020