Dear AI Network Community
What if we told you that you can train your next AlphaGo machine learning model with your private data on a peer-to-peer network, where workers compete each other to process your job and consequently you get to pay the lowest price possible in the market, without ever exposing your data or your model? You’d say it’s too good to be true, right? That’s because it is. At least for now.
AI Network is working toward building the global P2P platform on which machine owners can make better use of their idle computing power and ML researchers can develop their models with reasonable execution costs. However, with the amount of data that will be transferred back and forth between remote, unknown and untrusted machines, it’s not difficult to see that privacy is going to be one big hairy issue. And as far as privacy goes, it’s both the service provider and the data provider’s responsibilities to keep the data protected. So, AI Network dev team thought it’d be good to share some of the common threats to data privacy, as well as some countermeasures (plus their limitations).
In a recent publication “Privacy Preserving Machine Learning: Threats and Solutions”, Al-Rubaie et al. (henceforth “the authors”) categorize the possible privacy threats in ML into 4 types and propose techniques to achieve privacy-preserving ML (PPML). Although the paper doesn’t specifically deal with ML on cloud nor on a P2P network, the problems they discuss are part of the problems we as the dev team and the future participants of AI Network will have to tackle. The four categories of privacy attacks in ML discussed in the paper include:
reconstruction attacks, model inversion attacks, membership inference attacks, and
de-anonymization. Let’s take a closer look at each of them...
You can get more details from the link below.
https://medium.com/ai-network/a-brief-survey-on-privacy-preserving-machine-learning-techniques-b7883b5e6c33---------------------------------------------------
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