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# AnonPay

AnonPay helps to enhance privacy in online experiments. **For the first time in experimental history, AnonPay made it possible to safely collect payment details within the experiment.**

Included in this repository are slides of a recent talk on AnonPay as well as example experiments for oTree and z-Tree. In all, these materials should be sufficient to use these tools. If you have further questions, please contact us: <cler-team@uni-koeln.de>
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## Overview

There are 5 algorithms included in AnonPay:

- Privacy enhancement through *payment adjustment*
	1. ROD: Rounding off payment amounts
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	1. NUN: Ensuring nonuniqueness of payment amounts
	1. SPA: Enhance privacy for specially protected attributes
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- Privacy enhancement through *data erasure*
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	1. CPY: Separate payment details from behavioral dataset
	1. TDY: Redact the behavioral dataset
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ROD is not recommended (see the slides, p. 21; rounding is inefficient).

It is optional to use NUN and SPA. However, everyone should use CPY and TDY (which must always be used in combination) or similar techniques. If no sophisticated programming language is available, I recommend the "payment form methodology" (see slides, p. 8). This, while trivial to implement, is beyond the scope of AnonPay.
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## Example

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You can now find a [complete German-language example for oTree](https://gitlab.com/cler1/AnonPay_oTree_CLER). This is what we use in Cologne; note that this app only enables CPY and TDY as these are the most popular algorithms. If you are not from Cologne, you will need to adjust the verbiage on the last two pages.