Vikash Chaurasia, Scientist D in the Ministry of Electronics and Information Technology’s (MeitY’s) Cyber Laws and Data Governance Group, underscored that India’s push to implement the Digital Personal Data Protection Act (DPDPA) will centre on privacy-enhancing technologies (PETs) during Google’s ‘A Dialogue On Safe And Trusted AI’ held in Delhi on November 20, 2025. Notably, he described PET as “the core engine for us through which we can deliver implementation of the DPDP”. Furthermore, he emphasised that “the first thing for us on the agenda is PET”.
Chaurasia argued that organisations must solve privacy at the engineering layer, stating that companies “could resolve it at the engineering level more than at the user level”. He illustrated how engineering choices shape real-world privacy by noting that, when users once carried out online banking on laptops, receiving an OTP on a separate phone genuinely added a layer of security. But now, with both activities occurring on the same device, the OTP “doesn’t really give you any benefit but adds one process layer”.
He outlined MeitY’s early implementation priorities, including academic partnerships and programmes for “training the developer community” using existing PET kits.
Asit Kadyan, Deputy Director General of the Digital Twins Unit at the Department of Telecommunications (DoT), urged India to demonstrate PET use cases across stakeholders, noting the absence of the same, and emphasised that PET should work as “an ecosystem” rather than a single product.
How Privacy-Enhanced Technologies Work
PETs are becoming central to how major technology companies design data-driven systems, especially as AI models increasingly rely on vast quantities of sensitive information. PETs allow organisations to compute, analyse, and derive insights without exposing or directly accessing the underlying personal data.
As Evan Kotsovinos, Google’s Vice President for Privacy, Safety, and Security, explained at the dialogue, PETs are “sophisticated tools that allow us to perform analysis and computation without ever decrypting or exposing underlying data.” Moreover, he argued that PETs are essential for meeting the “ethical and legal duty to protect that data” when training modern AI systems that operate at a global scale.
During the event, Kotsovinos outlined three key PETs that Google considers foundational:
• Federated Learning: This decentralised approach trains models on the user’s device. Kotsovinos explained that Google sends the model to the device rather than sending the data back to the model. Only the model updates return to the server, meaning the data remains on the device at all times.
• Differential Privacy: This technique adds mathematically controlled noise to datasets. Kotsovinos likened it to hearing background sound at an event, where the collective noise protects an individual conversation while still…
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