Scribble Data’s Session 8 of “Making Data Science Work”

DATA PRIVACY BY DESIGN - Tally Case Study

Panelists:
Dr. Kalpit Desai, Founder, Datakalp

Facilitators:
Indrayudh Ghoshal (Scribble Data)
Venkata Pingali (Scribble Data)

References
* Privacy of Business Data – A Case Study from Tally Solutions
* The Privacy by Design clause of the PDP Bill 2019 (Chapter VI, Clause 22)
* Summary of the report of the committee of experts on Non-Personal Data

Experience at Tally
* What privacy by design is important? For whom?
* Why did they choose different paths for analytics and services?
* How did the privacy considerations impact your modeling roadmap?
* How did you communicate the approach taken by tally to end-users?
* What anonymization strategies did they use?

Larger lessons learnt
* How does privacy change the economics of modeling?
* How does privacy impact model design and development?
* How should data scientists prepare for the new regime?
* Could we audit the models for privacy violations? How should data scientists handle the risk?

Hosted by

Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more

Scribble Data’s Session 8 of “Making Data Science Work”

DATA PRIVACY BY DESIGN - Tally Case Study

Panelists:
Dr. Kalpit Desai, Founder, Datakalp

Facilitators:
Indrayudh Ghoshal (Scribble Data)
Venkata Pingali (Scribble Data)

References
* Privacy of Business Data – A Case Study from Tally Solutions
* The Privacy by Design clause of the PDP Bill 2019 (Chapter VI, Clause 22)
* Summary of the report of the committee of experts on Non-Personal Data

Experience at Tally
* What privacy by design is important? For whom?
* Why did they choose different paths for analytics and services?
* How did the privacy considerations impact your modeling roadmap?
* How did you communicate the approach taken by tally to end-users?
* What anonymization strategies did they use?

Larger lessons learnt
* How does privacy change the economics of modeling?
* How does privacy impact model design and development?
* How should data scientists prepare for the new regime?
* Could we audit the models for privacy violations? How should data scientists handle the risk?

Hosted by

Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more