Software trial available on request. I am developing a data privacy software detailed in provisional patent US 62/822,850 that privatizes individual data to be shown in a way that minimizes risk of data privacy leak. The software uses methods in an Differential Privacy to do so, except that it applies them in a way such that it would show individual data rather than aggregated statistics on data (e.g. average, sum, etc), which is what most, if not all, modern methods in privacy do today. Thus, we can provide much more depth of information and therefore much more value to data-holding companies without sacrificing privacy. It disrupts Data Science because now companies will not have to invest in having their own Data Science teams to look at their own secure data. They can outsource the data that we privatize for any analysis because we allow giving privatized individual data in a way that is accurate to the original dataset, as opposed to the current approach of providing only statistical aggregated results for groups of data.

1. Data protection has never been
more relevant than in today’s
data-driven world
#privacymatters

2. 39%of
companies
globally
experienced a
data breach in
20181 - and seems
to be rising at an
exponential rate2
At least
20%
in revenue
loss3
Stock
underperformance of
up to 15.6%
after 3 years5
Expected Loss of
at least
7.8% of revenue
and up to
€7.8 million in fines
and up to
6.1%in stocks after 3 years
What happens if you do not protect data
&
{ }&
Fines up to
4%
of global
revenue4

3. Data Protection
Security Privacy
Can protect data from
breaches, but there is still a
chance it will get breached.
Even if security is breached, it still can protect data
while providing accurate information.
Limited access behind lock
and key because very
limited number of people
are entrusted with the key.
Anyone can access the protected data =>
Finding better business solutions faster for data-
holding company by enabling more cooperation
and competition for best analytics solutions
Privacy is better value prop for business and markets
Why Privacy instead of Security?

4. A real life example
• A Car Rental company is trying to privatize its data so that
when they give this data to an Analytics company, it would
not leak the privacy of an individual customer. However, the
Analytics company wants to learn accurate patterns from the
Car Rental company’s client base to provide more business
value to the Car Rental company.
• Car Rental company had categorical (label) data about its clients
rentals.
• It wanted to focus on which cars to buy more and who to market to in
order to increase profit.

5. Let us take this to be the Car Rental company’s data*
MVP Demo
Ford
Honda
Cadillac
Note: Solution is NOT
restricted to only 2
numerical attributes.
This example is used to
easily understand our
product.
In general, current MVP
can operate over an
unrestricted number of
both categorical and
numerical attributes with
categorical outputs.

6. Obfuscated data
Data is noised up in
input attributes and
output labels.
MVP Demo

7. Obfuscated Data
MVP Demo
Since obfuscated points can be linked to
individuals in the original data that are
identifiable from a group or in a certain region…
Original Data

8. Suppressed data (shown in orange)
MVP Demo
…we suppress them
(not show them).

9. Returned Data
MVP Demo
We operate with
categorical input
attributes in a similar
way.

10. Returned Data
Comparison
As you can tell…
• Vulnerable parts of the original data cannot be reverse engineered
• Any individual changes to the original dataset will not affect any statistics
of the returned data
Original Data

11. WHAT OTHER PRIVACY COMPANIES DO
Start – Original Data
Obfuscation
Count of label/
Most prevalent label
End – Aggregated Result
Some Limited Information Query
− Not much information
Scenario A:

12. WHAT OTHER PRIVACY COMPANIES DO
Start – Original Data
Obfuscation
Count of label/
Most prevalent label
End – Aggregated Result
Some Limited Information Query
− Not much informationScenario B:

13. WHAT WE DO
Start – Original Data
Obfuscation
Suppress
privacy-
violating
obfuscated
data
End – Privatized Dataset Result − Provides depth of info

14. Disruptive Impacts
• Compared to what other data privatization methods do, we
allow any data analyst to receive the entire data so that they
can analyze it however they want to.
• Companies can reduce overhead costs by safely outsourcing
the data analysis without restricting how the data is analyzed
or without giving up too much accuracy in the results.

15. Milestones
March 2019 Started business and acquire startup advisor
April 2019 Invited by SV Sandhill Road investors regarding potential
May 2019 Trial version released

16. Let’s build a better protected tomorrow!
Email: banerjeearjun73@gmail.com
LinkedIn: https://www.linkedin.com/in/arjunbanerjee77/
Residence: Bay Area, CA, USA