aron/awebsite
aron/awebsite
1
0
Fork 0
Code Issues Pull requests Wiki Activity Actions

more translation

Browse source
This commit is contained in:
Aron Petau 2025-10-06 15:04:55 +02:00
parent 8ee055bfa8
commit 2ce9ca50b5
459 changed files with 21001 additions and 9395 deletions
Download patch file Download diff file Expand all files Collapse all files

54
content/project/2023-03-01-ruminations/index.md
View file

@ -29,38 +29,48 @@ show_shares = true
+++
## Ruminations
was a contemplation on data privacy at Amazon.
It asks how to subvert browser fingerprinting and evading the omnipresent tracking of the consumer.
This project explores data privacy in the context of Amazon's ecosystem, questioning how we might subvert browser fingerprinting and challenge pervasive consumer tracking.
The initial idea was to somehow, by interacting with the perpetrator and letting data accumulate that would degrade their knowledge and thereby destroy predictablity, making this particular dataset worth less.
We began with a provocative question: Could we degrade the value of collected data not by avoiding tracking, but by actively engaging with it? Rather than trying to hide from surveillance, could we overwhelm it with meaningful yet unpredictable patterns?
We could have just added a random clickbot, to confuse things a bit and make the data less valuable.
But looking at todays state of datacleanup algorithms and the sheer amount of data that is collected, this would have been a futile attempt. Amazon just detects and removes any noise we add and continues to use the data.
Initially, we considered implementing a random clickbot to introduce noise into the data collection. However, given the sophistication of modern data cleanup algorithms and the sheer volume of data Amazon processes, such an approach would have been ineffective. They would simply filter out the random noise and continue their analysis.
So, then, how can we create coherent, non-random data that is still not predictable?
One answer that this concept should demonstrate, is by inserting patterns that amazon cannot foresee with their current algorithms. As if they were trying to predict the actions of a person with shizophrenia.
This led us to a more interesting question: How can we create coherent, non-random data that remains fundamentally unpredictable? Our solution was to introduce patterns that exist beyond the predictive capabilities of current algorithms – similar to trying to predict the behavior of someone whose thought patterns follow their own unique logic.
## The Concept
It consists of a browser extension (currently Chrome only) that overlays all web pages of Amazon with a moving entity that tracks your behavior. While tracking, an image classifier algorithm is used to formulate a product query off of the Storefront. After computation, a perfectly fitting product is displayed for your consumer's pleasure.
We developed a Chrome browser extension that overlays Amazon's web pages with a dynamic entity tracking user behavior. The system employs an image classifier algorithm to analyze the storefront and formulate product queries. After processing, it presents a "perfectly matched" product – a subtle commentary on algorithmic product recommendations.
## The analogue watchdog
## The Analog Watchdog
A second part of the project is a low-tech installation consisting of a camera (we used a smartphone) running a computer-vision algorithm tracking tiny movements. This was then pointed towards the browser console in the laptop running the extension. The camera was then connected to a screen that displayed the captured image. The watchdog was trained to make robot noises depending on the type and amount of movement detected. Effectively, whenever data traffic beween amazon and the browser was detected, the watchdog would start making noises.
The project's physical component consists of a low-tech installation using a smartphone camera running computer vision algorithms to track minute movements. We positioned this camera to monitor the browser console of a laptop running our extension. The camera feed is displayed on a screen, and the system generates robotic sounds based on the type and volume of detected movement. In practice, it serves as an audible alert system for data exchanges between Amazon and the browser.
# The Browser extension
## Implementation
gallery:
![The project installation](ruminations1.jpeg)
![The project installation](ruminations2.jpeg)
![The project installation](ruminations3.jpeg)
{% gallery() %}
[
{
"file": "ruminations1.jpeg",
"alt": "Project installation view showing the browser extension in action",
"title": "The Ruminations installation in operation"
},
{
"file": "ruminations2.jpeg",
"alt": "Close-up of the tracking interface and data visualization",
"title": "Real-time tracking visualization"
},
{
"file": "ruminations3.jpeg",
"alt": "The analog watchdog setup with camera and display",
"title": "The analog watchdog monitoring system"
}
]
{% end %}
## Try It Yourself
### Find the code on GitHub
Want to explore or contribute to the project? Check out our code repository:
Subvert a bit yourself, or just have a look at the code.
[The code of the Project on GitHub](https://github.com/arontaupe/ruminations)
TODO: create video with live demo
<div class="buttons centered">
<a class="big colored external" href="https://github.com/arontaupe/ruminations">View Project on GitHub</a>
</div>