Personal data anonymization: key concepts & how it affects machine learning models
Consultancy company Tryolab published an in-depth piece about data anonymization techniques. It's got everything you need to know about the different methods and models of data anonymization.
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Privacy-preserving A.I. is the future of A.I.
This month, Fortune takes a look at the application of machine learning in a privacy-preserving context. The authors reflect on the possible applications and methods to protect individuals’ privacy in AI systems.
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When fake is good news: synthetic data enabling usage and privacy
Day 2 of the Transparency by Design Summit was opened with a keynote from Vince Madai, Senior Medical AI Researcher at Charité Berlin on the topic of synthetic data and its possibilities for the healthcare industry. The replay is available online.
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Just how well anonymized is anonymized data?
We weren’t the only ones questioning the anonymity of anonymized data this month. Swiss Re Institute, IBM Research, and the Gottlieb Duttweiler Institute teamed up to discuss the limits of data anonymization and shared a written summary of their online discussion.
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Lessons from the PULSE Model and Discussion
A recently released “Face Depixelizer” built from a deep generative model, caused some heated discussions among AI researchers, particularly the bias found in the model. Like the author of this post, we believe such matters are important to discuss and share.
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