Synthetic data has been advertised as a silver-bullet solution to
privacy-preserving data publishing that addresses the shortcomings of
traditional anonymisation techniques. The promise is that synthetic data drawn
from generative models preserves the statistical properties of the original
dataset but, at the same time, provides perfect protection against privacy
attacks. In this work, we present the first quantitative evaluation of the
privacy gain of synthetic data publishing and compare it to that of previous
anonymisation techniques.

360 Mobile Vision - North & South Carolina Security products and Systems Installations for Commercial and Residential - $55 Hourly Rate. ACCESS CONTROL, INTRUSION ALARM, ACCESS CONTROLLED GATES, INTERCOMS AND CCTV INSTALL OR REPAIR 360 Mobile Vision - is committed to excellence in every aspect of our business. We uphold a standard of integrity bound by fairness, honesty and personal responsibility. Our distinction is the quality of service we bring to our customers. Accurate knowledge of our trade combined with ability is what makes us true professionals. Above all, we are watchful of our customers interests, and make their concerns the basis of our business.

Our evaluation of a wide range of state-of-the-art generative models
demonstrates that synthetic data either does not prevent inference attacks or
does not retain data utility. In other words, we empirically show that
synthetic data suffers from the same limitations as traditional anonymisation

Furthermore, we find that, in contrast to traditional anonymisation, the
privacy-utility tradeoff of synthetic data publishing is hard to predict.
Because it is impossible to predict what signals a synthetic dataset will
preserve and what information will be lost, synthetic data leads to a highly
variable privacy gain and unpredictable utility loss. In summary, we find that
synthetic data is far from the holy grail of privacy-preserving data

By admin