PVML has emerged from stealth mode with an $8 million seed funding round! Read more here.

Differential Privacy Methodology

Differential Privacy is a mathematical framework that offers the strongest data safeguard in data-driven systems. It achieves this by adding controlled noise to the output of a query or algorithm, making it statistically indistinguishable whether a particular individual’s data was included or excluded. This nominal amount of variance is insignificant in a broader analytical context, but is crucial to create a robust barrier against attempts to extract sensitive information.

Less Configurations,
More Value

Grant access to full data

Unlock previously off-reach data by giving
users access with Differential Privacy for
safe analytics.

Eliminate the grey area

Achieve automated privacy at the output level,
providing secure access to third parties, and enabling
the analysis of even the most sensitive data such as
locations, financial, and healthcare information.

simple and cost effective

No need for PII tagging; no encryption, masking, or
redacting; and no data duplication or movement are
required when using Differential Privacy.

Full SQL Support
with Differential

We developed a data protection engine that transforms SQL queries interacting with the target data source, ensuring that the output preserves Differential Privacy (DP).
Real Time
DP is applied on-the-fly in real time
No need to change query syntax
Analysis Consistency
Advanced caching to ensure
analysis consistency

Shifting the Paradigm
of Anonymization

PVML incorporates beyond state-of-the-art research
objectives alongside software engineering in order to
provide cutting-edge Differential Privacy (DP) algorithms.

Protection Scope

Data Utility
vs. Data Protection

Differential privacy balances information needs with
the risk of exposing sensitive data, enabling
meaningful analysis while safeguarding critical details
about individuals.

to Attribute Inference

DP treats all data columns as sensitive, preventing
attackers from inferring private information about an
individual by combining different non-sensitive pieces.

Engineering Immunity

The ‘post-processing immunity’ of DP offers a strong
defense against adversaries trying to deduce
sensitive information from analysis outputs, even with
access to auxiliary data.

with Regulations

The European Data Protection Board’s Article 29 has
affirmed that DP is the sole method for achieving true
anonymization after reviewing 7 approaches.

Tech Giants’

  • 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. Learn More

  • 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. Learn More

  • 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. Learn More


PVML. Data Peace
Of Mind.

Experience the freedom of real-time
analytics and the power of data
sharing, all while ensuring
unparalleled privacy.