Notes on Adversarial Machine Learning

In this post I collect some disorganized notes on the topic of the security of machine learning algorithms.

These notes are a result of skimming some papers while preparing a lecture on the topic, as well as some random accompanying thoughts.

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Solving the Cyber-Security Bubble

There seems to be a big bubble in cyber security. Many awful products in the market, and many bad startups easily raising funds. I believe this problem needs to be addressed, either by governmental regulations or by independent companies. In this brief post I lay out the problem and what has been done so far to mitigate this effect.
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Security of Google’s Federated Learning

In this post I’ll collect some initial thoughts regarding the security of Google’s Federated Learning, which is a method for learning a model on a server where the clients do not send their data, but instead they send an updated model trained on their device with their own data.  The main points are:

  1. Knowing the clients update can give information on his training data.
  2. Knowing the average of some updates is likely to give information on each user’s update.
  3. If an attacker can send many updates, he can get information on a specific client.

The first two points are acknowledged briefly in the article.

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