Differential Privacy (DP) is a vital concept for protecting personal data. It was created to allow organizations and researchers to apply analytics and machine learning methods on sensitive data while preserving the privacy of the individuals in the datasets.
Differential Privacy algorithms require a mathematical proof that the result of any computation performed on a dataset is identical (or at least very close) whether any individual was part of the dataset during analysis or not.Read more about Differential Privacy on our blog
Differential Privacy guarantees the same output probability whether any individual was part of the data or not.
Differential Privacy offers the strongest possible privacy protection available for working with sensitive data, with a mathematical proof that guarantees individual-level privacy. This is done by introducing just enough randomization (statistical noise) during computation, masking the contribution of any single individual’s record and providing the highest level of privacy protection. This nominal amount of variance is insignificant in a broader analytical context, but is crucial to guarantee privacy.
Differential Privacy is already being utilized by tech giants Google, Apple and Microsoft for their unique competitive edge (but is not offered as a service):
We are continually expanding our Differential Privacy technology across our products including Google Maps and the Assistant ... Last year we published our COVID-19 reports which use Differential Privacy to help public health officials, economists and policymakers.
The information we gather using Differential Privacy helps us improve our services without compromising individual privacy ... Analysis happens only after the data has gone through privacy-enhancing techniques like Differential Privacy.
How do we create ML models that preserve the privacy of individuals while including the broadest possible data? Differential Privacy simultaneously enables researchers and analysts to extract useful insights ... and offers stronger privacy protections.
Applicability is our first priority: PVML incorporates beyond state-of-the-art research objectives along with software engineering and applied machine learning in order to provide the most efficient Differential Privacy algorithms.
We developed cutting edge technology for data analytics, training machine learning models and synthetic data generation, all using proprietary Differential Privacy algorithms that produce privacy-preserving results with high accuracy guarantees.
query the data and drill-down freely, no query is off-limits and no need to use query auditing or think twice before performing a query.
train machine learning models with no reverse-engineering concerns (model inversion attacks).
integrate our Differential Privacy algorithmic capabilities with an existing interface without altering current practices.
generate high fidelity synthetic data with Differential Privacy guarantees.
Have other questions? Contact us!