The paper entitled "On the Development of A Metric for Quality of Information Content over Anonymised Data-Sets" was presented at the excellent Quatic 2016 conference held in Lisbon, Sept 6-9, 2016.
We were also extremely fortunate in that a presented in our session didn't turn up and we were graciously given the full hour not just to present the paper but give a much fuller background and details of future work and the state of our current results.
Here are the slides:
On The Development of A Metric for Quality of Information Content over Anonymised Data Sets from Ian Oliver
Abstract:
We propose a framework for measuring the impact of data anonymisation and obfuscation in information theoretic and data mining terms. Privacy functions often hamper machine learning but obscuring the classification functions. We propose to
use Mutual Information over non-Euclidean spaces as a means of measuring the distortion induced by privacy function and following the same principle, we also propose to use Machine Learning techniques in order to quantify the impact of said obfuscation in terms of further data mining goals.
Citation:
Ian Oliver and Yoan Miche (2016) On the Development of A Metric for Quality of
Information Content over Anonymised Data-Sets. Quatic 2016, Lisbon, Portugal, Sept 6-9, 2016.
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