ML Feature Store Enhancements for PDP Compliance

Jan 2021

18 Mon

19 Tue

20 Wed

21 Thu

22 Fri 10:00 AM – 12:00 AM IST

23 Sat

24 Sun

Concerns about privacy are growing mainly because:

The costs of compliance have increased, including monetary fines and penalties for non-compliance to regulations - GDPR, CCPA, sector-specific rules such as for health, fintech and social media platforms.
Privacy is important to build trust with users, and for user retention.
Growing awareness about privacy and demand from customers for businesses to guarantee privacy of their data.

These, and other imperatives - business, regulatory, governmental - are the basis for the Privacy Engineering Conference.

Plan for the Conference: Scheduled for January 2021, this conference will have the following build-up activities:

Bi-monthly meetups, leading to the build-up of the conference.
Public talks - by privacy engineers - to help us push the boundaries of engineering approaches and solutions.
Round tables and Birds of Feather (BOF) sessions - for targeted domains and segments to discuss specific concerns, and how cross-pollination can occur for building towards a community for privacy enhancing technologies.
Collaborations to create content such as cheatsheets, case studies, checklists and guides, which will help the tech industry in efforts for building privacy-tech.
Workshops.
Other activites as per needs, demand and topicality.

The Call for Proposals (CfP) is open. Presentations submitted via the CfP will be funneled to the build-up sessions.

Participate in Communities: If you are concerned/interested in discussing privacy, engineering and design issues, consider participating in groups such as:

These groups/communities actively discuss privacy, data governance and privacy-tech issues, including organizing events on topical issues.

You can also join The Fifth Elephant’s Telegram group here: https://t.me/fifthel to talk to data scientists and ML engineers on privacy-tech (among other topics).

Contact information: For queries, write to fifthelephant.editorial@hasgeek.com or call 7676332020.

Hosted by

The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

All submissions

ML Feature Store Enhancements for PDP Compliance

Submitted Jun 6, 2020

Session format: Talk - 40 mins

The new PDP (Personal Data Protection) Law, which is similar to GDPR
and CCPA, will be passed in the near future and will go into effect
immediately. All enterprise data services including analytics and data
science within the scope of the law are required to comply with the
same.

Enrich, our product, is a customizable ML feature store for mid-market
enterprises that provides datasets to analysts and models at scale
everyday. Such data preparation services are on organizations’
compliance and privacy-activity critical path because of their
‘fan-out’ nature. They provide a convenient location to enforce policy
and safety mechanisms.

In this talk we discuss some of the compliance mechanisms that we are
building for clients. They include opensource compliance checklist to
help with the process, inventory classification service, ‘right to
forget’ service using anonymized lookup key service, and metadata
service to enable tracking of the datasets. The focus will be on the
generic capabilities, and not on Scribble or our product.

Outline

PDP and Impact
- Quick overview of PDP
- Key provisions with architectural significance
Feature Store Basics
- How we see the bill
PDP-related Extensions
- Compliance checklist & tracking
- Data inventory & classification
- Data Quality
- Consent manager & Data sanitization
- Logging and metadata
Open Challenges
- Extending to enterprise beyond ML data prep
- Uncertainties

Requirements

High level understanding of ML model development process

Speaker bio

Dr. Venkata Pingali is Co-Founder and CEO of Scribble Data, an ML Engineering company with offices in India and Canada. Enrich, Scribble Data’s customizable ML Feature Store, enables organizations to address 10x analytics/data science use cases through trusted production datasets. Before starting Scribble Data, Dr. Pingali was VP of Analytics at a data consulting firm and CEO of an energy analytics firm. He has a BTech from IIT Mumbai and a Ph.D. from USC in Computer Science.