Anonymizer
Revitalize valuable but unavailable data
The Long Unsolved Trade off:
Data Utility vs. Privacy
De-identified data meet privacy regulations
Privacy regulations, such as GDPR, are applied to information that are relatable to an identifiable person. Therefore, utilizing anonymous data or
de-identified information is not subject to legal
regulations.
Existing technologies make data unusable
Existing de-identifying technologies in the industry significantly limit the capability of utilizing data.
Since they simply detect and delete personal
information, all the other key attributes for machine
learning and analyzing are also erased.
Anonymizer achieves both
and this what that makes the big difference
Then, what is it that makes Anonymizer so different
from other de-identifying technologies?
Anonymizer allows companies or ML developers to
collect data that are usable for their target uses
but also guarantee privacy. It is the only possible way
to achieve both data utility and privacy regulation
compliance.
While removing Personally Identifiable Information(PII),
Anonymizer preserves data quality which is equivalent
to the original. As data are anonymized, they become
invisible to human but visible to AI, allowing users to
train actual ML models while ensuring other's privacy.
The big change that only Anonymizer can bring is to
develop machine learning models without using
original data.
Anonymizer
The Innovation Process
Building a new ML model without using original data
Anonymizer
First, anonymize original data
Anonymizer obfuscates data task-specifically for users. For instance, a data consumer who wants to build a cat detecting ML model is provided with anonymized data without any private information but with key attributes necessary for the cat detection.
Sharing Anonymized Data
Model G
"cat"
Second, train a new model
With anonymized data provided by Deeping Source, users can train a new ML model(G) whose output is nearly identical to that of the original data.
Deploy Model G
Model G
"cat"
Third, deploy the model in actual cases
Trained with anonymized data, model G is highly useful in actual environments where new original data are collected - that is to say, if anonymized with our Anonymizer, users can develop actual ML models even with anonymized data.
Obfuscator
The safest way to use data with confidential information
Confidential data
that needs to be shared
Confidential data contains classified or
sensitive information that only authorized
persons can fully access, and which may cause
critical damage to the organization when leaked.
This data, essential information of the
organization, requires high level security
and careful management when being shared and utilized for purposes such as building an AI model.
Obfuscator as a solution
A reliable way of sharing confidential data
Obfuscator hides confidential information by making data unreadable while preserving their utility for machine learning.
Only the necessary features needed for target uses remain.
All the other data components are obfuscated and cannot be reversed to the original.
In other words, Obfuscator allows users to train
ML models without using the original data.
Therefore, our users don't have to be concerned
even if it is handed to a third party who is
not fully authorized for accessing the original information.
Obfuscator is the solution for organizations who are seeking new opportunities via data driven AI technologies.
Obfuscator
