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Rumanu

@rumanu

Applying Differential Privacy to India's Census Data

Submitted Feb 2, 2024

Category: AI for India

Bringing Technically Ensured Privacy to Indian Data in the Public Domain

Modelled after The US Government’s attempt to release 2020 Census data in a differentially private manner, the project aims to successively apply Differential Privacy to 0.001%, 0.01% and 0.1% of India’s census data to be able to release such personal data that allows us to obtain aggregate statistics about the trends in the population without compromising entirely on individual privacy for the members involved. The attemp wants to define mathematically rigourous limits for the level of guaranteed privacy in such instances so make the approach measurable instead of qualitative in nature.

This is an experimental approach to estimate the feasibility of attempting this at scale and checking to see if insights drawn still serve the purpose of drawing demographic insights that allow the governance to gauge and serve the needs of its population in all its diversity.

The project also aims to assess whether the analysis thus drawn would be compliant with the terms proposed by the Personal Data Protection Bill of India, if such a dataset were acquired by consumers of a startup as a starting point to see the efficacy of DP as a solution to business analysis needs.

The solution will involve using PyTorch’s Opacus, Tensorflow’s Privacy tools as well as OpenMined’s PyDP and Harvard’s OpenDP libraries, all open source, as the solution demands, based on the merits and demerits of each library’s speed, design and ease of use.

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