A New Era
of Data Protection
Differential Privacy Methodology
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.
Operationally
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.
How does it work?
for all female employees
the highest salary
SELECT AVG (Salary)
FROM Employees
WHERE Gender == ’Female’;
SELECT EmployeeName, JobTitle, Salary
FROM Employees
ORDER BY Salary DESC
LIMIT 1;
Full SQL Support
with Differential
Privacy
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.
Resilience
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.
Reverse
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.
Compliance
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’
Stamp-of-Approval
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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
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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
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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
