About

Participated

• ETH London - 15th March 2024 - Submission
• ETH Oxford - 8th March 2024 - Submission

Supported

• ETH Berlin 2024
• MoneroKon 2024
• Weekly Web3 Workshops
• On Chain and OSINT analysis on 2017 Parity Hack - Might Blog Post it

eps1.0_hellofriend.mov

Professional automater, obsessed with data and decentralisation.

As you might have guessed from the title, I am a fan of Mr Robot. Apart from being an amazing show, with outstanding visuals and audio, the plot is very convoluted. The concept of a small talented group having the ability to bring down an ““establishment” has always appealed to me. I guess it is a bastardised version of the American Dream. Regardless, it was mainly because of this show, and other stuff I pursued studying Cyber Security at University, it was within the first month I realised that it isn’t as trivial to replicate the plot.

Coming to the realisation that taking over a country and ruining its economy is too much effort. I was introduced to more cyber-resilient systems, Peer to Peer networks, Distributed computation and more. This is where I was properly introduced into blockchain technologies. Understanding this system may be a lot more valuable in the future made even more doubtful of viability of Mr. Robot V2. Regardless, it’s a good TV show show I recommend that you should definitely watch it (you’ve already stumbled on this page).

How I got into ML, well my interest in cyber mainly pertains to privacy, and ML mainly requires a lot of data. Therefore, I was interested in Privacy Preserving machine learning solutions PPML. The terabytes of high variance data needs to come from somewhere. So my main interest within the ML space is Federated Learning (FL). Where you get the best of both worlds. The automation, flexibility and general value of ML without intruding on the privacy of the masses you need the data from. This should also encourage more people to actively share their data, since in theory the data should never leave their device. So to learn more I jumped in the deep end, for my University research I made ML models using homomorphic encryption. ML or Homomorphic encryption was not covered within the course at all. Regardless I just wanted to learn about stuff that actually interested me, rather than getting a high grade by doing a generic dissertation on networks.

In short, we’re losing to much accuracy when training models, creating models and then encrypting it (inference only) is boring because you already did the deed (using a bunch of peoples data). So I was interested in other avenues, like ZK which has specific applications and Secure multi party computing.