Archive for the 'privacy and security' Category

In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Ocean Protocol.

As mentioned in the show, this is a picture that provides a 10000-feet view of the integration.

 SEA-ocean-fitchain.png

 

I hope you enjoy the show!

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In this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress :) - one providing secure machine learning on isolated data, the other implementing a decentralized data marketplace.

In this episode I explain:

  • How do we make machine learning decentralized and secure?
  • How can data owners keep their data private?
  • How can we benefit from blockchain technology for AI and machine learning?

 

I hope you enjoy the show!

 

References

fitchain.io decentralized machine learnin

Ocean protocol decentralized data marketplace

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In this episode I don't talk about data. In fact, I talk about metadata.

While many machine learning models rely on certain amounts of data eg. text, images, audio and video, it has been proved how powerful is the signal carried by metadata, that is all data that is invisible to the end user.
Behind a tweet of 140 characters there are more than 140 fields of data that draw a much more detailed profile of the sender and the content she is producing... without ever considering the tweet itself.

 

References
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information https://www.ucl.ac.uk/~ucfamus/papers/icwsm18.pdf

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Attacking deep learning models

Compromising AI for fun and profit

 

Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.

In this episode, we explain how machine learning models can be attacked and what we can do to protect intelligent systems from being  compromised.

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Humans seem to have reached a cross-point, where they are asked to choose between functionality and privacy. But not both. Not both at all. No data, no service. That’s what companies building personal finance services say. The same applies to marketing companies, social media companies, search engine companies, and healthcare institutions.

In this episode I speak about the reasons to aggregate data for precision medicine, the consequences of such strategies and how can researchers and organizations provide services to individuals while respecting their privacy.

 

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Data is a complex topic, not only related to machine learning algorithms, but also and especially to privacy and security of individuals, the same individuals who create such data just by using the many mobile apps and services that characterize their digital life.

In this episode I am together with B.J.n Mendelson, author of “Social Media is Bullshit” from St. Martin’s Press and world-renowned speaker on issues involving the myths and realities involving today’s Internet platforms.  B.J. has a new a book about privacy and sent me a free copy of "Privacy, and how to get it back" that I read in just one day. That was enough to realise how much we have in common when it comes to data and data collection.

 

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Talking about security of communication and privacy is never enough, especially when political instabilities are driving leaders towards decisions that will affect people on a global scale

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Data science is making the difference also in fraud detection. In this episode I have a conversation with an expert in the field, Engineer Eyad Sibai, who works at iZettle, a fraud detection company

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Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that you must try before further analysis (Part 1).

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