Final blow before Tea! Atif Rahman Twitter: @mantaq10 Zetaris - - PowerPoint PPT Presentation

Privacy Preserved Data Augmentation using Enterprise Data Fabric

Final blow before Tea!

I was like her according to her; We were both outliers Twitter: @mantaq10 Atif Rahman Zetaris www.zetaris.com

Data Exchanged (without consent)

• GPS
• HIV Status
• Email addresses
• Weapon: Contract
• Response: Excuse
• Exposure: (Potential) exposure
• f marginalized people.

Data Breach:

• Email Addresses
• Username & Passwords

Exposure:

• 150 million customers

Response:

• No clear Apologies
• (Delayed) Corrective Actions

Weapon: Contract

Data Breach:

• Names
• Loyalty data
• Email addresses
• Physical addresses
• DOB
• Credit Card last 4 digits

Exposure:

• Millions of Customers

Response:

• Denial
• Fake Solutions
• 8 months before first action

Paper contracts are still the most common weapon organizations use to get away with. As regulations get more mature, the impetus to be more effective in privacy preservation will be on service providers.

From the exhibition: "M. Hulot, the protagonist in Jacques Tati's 1967 film Playtime, is continually frustrated by the endless repetition of office cubicles.

Enterprises have different data landscape than consumer facing (typically tech) organisations. Enterprises have silos, legacy systems, have to learn to be data driven the hard way and have divergent forces giving a unique focus on

Agenda

• Data Augmentation
• First Principles
• Enterprise Data Fabric

Data Augmentation

ORG A Class 1 Class 2 Class 3

Data Augmentation

ORG A Class 1 Class 2 Class 3 ORG A Class 1 Class 2 Class 3 ORG B ORG C

Potentially Better

Typical Modeling Exercise Modeling after data augmentation

ORG A Class 1 Class 2 Class 3 ORG B ORG C

Content Shared

• Aggregated Data / Insights
• Open Data
• Stratified Sampling
• Synthetic Data
• De-identified / Anonymized

Channels:

• Public Portals
• Private Marketplaces
• In Person Walk

throughs/handovers

• Gossiping
• Pigeons

Data Augmentation

Data as an asset

• Easy to copy and spawn
• Does not depreciate or depletes
• Really hard to valuate
• Process to yield value
• Various forms and derivatives

Resolve to First Principles

Data has properties that make it intrinsically hard to ensure privacy

• preservation. Therefore, we must

adhere to first principles to better understand the problem statement first.

The Five Safes

Safe Data Safe People Safe Setting Safe Project Safe Output

Great Resources

ACS Data Sharing Frameworks The De-Identification Decision Making Framework

First Principles

Safe Data Safe People Safe Setting Safe Project Safe Output Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

First Principles

Safe Data Safe People Safe Setting Safe Project Safe Output Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

Safe Data – (Encryption)

Data at Rest Standard Encryption Data in Transit Secure the Pipe Data for Compute Homomorphic Encryption

Homomorphic Encryption

Partial Homomorphic Encryption (PHE) Somewhat Homomorphic Encryption (SWHE) Full Homomorphic Encryption (FHE) Addition/Multiplication Low Order Polynomials Eval of Arbitrary Functions

More General Less Costly

Data Analytics without seeing the data Max Ott, YOW Data 2016

First Principles

Safe Data Safe People Safe Setting Safe Project Safe Output Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

Safe Setting - Confidential Computing

Trusted Execution Environments (Safe Data in Safe Setting)

Microsoft Azure Confidential Computing Google Cloud Platform: Asylo Open Source Framework Confidential Computing at the Software layer?

First Principles

Safe Data Safe People Safe Setting Safe Project Safe Output Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

Alice Bob

Safe People – (System Span)

Safe People – (System Span)

First Principles

Safe Data Safe People Safe Setting Safe Project Safe Output Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

Safe People – (System Span)

Safe People – (System Span)

Expanding the Span of control

First Principles

Safe Data Safe People Safe Setting Safe Project Safe Output Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

Safe Project – Audit Trails & Lineage

Safe Project – Audit Trails & Lineage

?

Data in the wild

Its still very hard within enterprises to have a point to point track of data lineage and processing. The problem is expounded when data leaves the span of vision.

One Ring to Rule them All?

Encryption Authentication & Authorisation Environment for Data Controllers & Processors Audit Trail, Lineage and Access & Query Logs Linkage Problem

A data landscape must cover all principles of data privacy.

Monoliths in the era of Microservices

DB Server App

App DB Server DB Server DB Server

App DB Server DB Server DB Server DB Caching DB In-Memory DB Streams DB Messaging App App

DB Server App Server Server DB DB DB App App The Enterprise Data Fabric A unified data layer that is used by both user facing applications and downstream analytics, a potential holistic five safes environment

The Zetaris Enterprise Data Fabric – Location Aware, Usage Aware, People Aware, Privacy Preserved data in a secure environment. Also check out Apache Ignite, Redhat OpenShift + JBoss Virtualization,.

GDPR Highlights

Data Portability Erasure Access Consent

Right to transfer personal data from one electronic processing system to and into another. Right to withdraw consent and ask for personal data to be deleted Right to know what’s been collected and how its being processed Consumer is informed in ’clear’ and plain language. Consent to collect can be withdrawn at any time By Design By Design By Design By Design Only through Serialization Random writes are not typical Limited Purview Hard

Monoliths e.g. Lakes Data Fabric

As data scientists, we are at the forefront of disruption and hold the potential to change things. We are automating decisions in all aspects of society. Yet, our work has serious negative implications, we need to educate ourselves

• n the broader societal

questions around regulations, ethics and impact Enjoy the Tribe!