Privacy and Data Science - PyData London

Privacy and Data Science - PyData London

Data science on customer data opens up huge opportunities, both for economic benefit and social good. But as datasets become richer, individual privacy comes under threat, and indeed responsible organisations are blocked from innovating because they have no way to guarantee privacy.

Technology has created this problem, and technology can solve it.

I talk about Privacy Engineering techniques that enable the safe and effective use of data, including tokenisation and masking, statistical generalisation and blurring of data (such as k-anonymity), controlled privacy-preserving querying of data (such as differential privacy), homomorphic encryption and randomised response. I describe the state of the art, and outline the hard problems that must be solved next.

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Jason McFall

February 07, 2017
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