The reason why data anonymization really matters
AI industry is losing opportunities
Massive amounts of data are key assets for the AI industry. However, from selfies to medical records, most data include private information. Two greatest challenges when utilizing personal data are strict regulations and the enormous expense.
While data is essential to unleash the power of AI, protecting privacy is equally important. This conundrum has created a strong demand for a tool that delivers both.
Protecting Confidentiality
Some images, videos, texts or audio files contain critical information of an organization, which should not be revealed to the public.
When using or sharing data with confidential information, preventing all the possibilities of data leakage is a must.
Protecting Data Ownership
From uploading to labeling, validation and purchase, many people are involved in the data transaction process.
All the data need to be protected from unlawful data usages, such as illegal copy or reselling.
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

