The Fifth Elephant 2024 Annual Conference

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Gaurav Aggarwal

@gauravaggarwal

A privacy preserving DPI to unfreeze data markets - to solve our data woes!

Submitted May 23, 2024

Clearly, there is a race to build larger and larger AI models these days trained on as much training data as possible. Indian developers are also trying to make their presence felt in this race to build home-grown models for our unique problems and situations. Owing to our large population, we probably generate more data than any other country. This sounds great, right, but much of this Indian context data is not in one place and is hidden behind numerous government and corporate walls. What makes the situation worse is most of these data silos are enterprises of traditional nature and are not the typical centers of innovation, at least for modern technologies like AI.
This is a fertile ground for DPI (Digital Public Infrastructure) for AI . The three core concepts (Confidential Computing, Differential Privacy and Smart Contracts) of DPI for AI ensure that this data sitting in silos can be seamlessly and democratically shared with innovators around India in a privacy-preserving manner. The techno-legal framework makes it super easy for anyone to abide by the privacy regulations without sweat. This unfreezing of data is critical for our innovators to get easy access to contextual data to give them a much-needed leg up against the western onslaught in the field of AI. This is India’s chance to leapfrog in the field of AI as we have done in so many domains (payments, identity, internet, etc.).

Outline

  • DPI for AI as a credible solution to increasing data needs for AI model development
  • Overall architecture of the training architecture
  • Deep dive into concepts from Differential Privacy (DP) which is a core pillar that enables safe data collaboration

Impact

  • Understand privacy as part of the design construct instead of an afterthought when we train models
  • Get acclimitised to a new DPI which is currently getting rolled out and get a chance to become an early adopter for the same

Who is the audience for this?

  • AI practitioners who are wondering where the data will come from - particularly folks in startups.
  • Developers/product folks who build models using consumer data.

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