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?
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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
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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