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DEPA-Training: Tech Updates

We’ve rolled out some exciting updates for DEPA‑Training, making it easier to rapidly prototype and run diverse training scenarios — complete with electronic contracts, confidential cleanrooms, privacy-preservation and configurable training SDKs.

✨ What’s new

👉 GUI for end-to-end execution

👉 Step-by-step guide to create and run your own training scenarios

👉 New scenarios introduced for complex multi-party training: MRI brain tumor segmentation, credit default risk prediction

Before we dive in, let’s quickly recall what the Data Empowerment and Protection Architecture (DEPA) really is.

What is DEPA and why does it matter?

India Stack is evolving at population scale, enabling the flow of people (Aadhaar, eKYC, DigiLocker, DigiYatra, etc), money (UPI, OCEN), and information (DEPA and Account Aggregator) through Digital Public Infrastructure (DPI). DEPA is critical in this third layer as it enables the responsible flow of data between individuals and organisations for more complex tasks such as AI model training, AI inference and analytics. 

As the name suggests, DEPA rests on two key elements. The first is protection, founded on the bedrock of privacy, consent, accountability and purpose limitation of data. The second is empowerment, democratizing data access and enabling the ecosystem to responsibly innovate with it, whether for training AI models, personalizing products and services, advancing scientific research, and a lot more.

In light of emerging data protection laws such as the DPDP, GDPR, and others, there is a need for a framework that enables the responsible use of data — unlocking its value while ensuring regulatory compliance and serving the broader public interest.

Ultimately, DEPA solves for two core challenges at the heart of data sharing — Trust and Flow — keeping the rest open and flexible for innovation.

What is DEPA‑Training?

The vision behind DEPA for Training (aka DEPA‑Training) is simple: For India to not only be a consumer of AI, but also a producer of AI, and in a responsible and democratized manner.

AI’s first big leap came from public data. That well is running dry. Our belief is that for the next wave of AI innovation — smarter AI for healthcare, personalized finance, scientific discovery and more — proprietary data will be crucial. But today, that data is fragmented, locked in silos, and difficult to use — often running into challenges around privacy, compliance, and regulatory constraints.

Enter DEPA-Training — a techno-legal Digital Public Infrastructure (DPI) designed to enable secure, agile, and scalable AI model training on sensitive data. It does so by assembling a set of frontier technological primitives:

  • Confidential Clean Rooms (CCRs): Isolated compute environments that can cryptographically attest to their integrity, where data can be processed securely without external exposure.
  • Electronic Contracts: Code-enforced legal agreements between transacting parties, that give data providers control over how their data is used, for eg. through purpose limitation, privacy safeguards and monetization.
  • Secure Training Sandbox: Modular and configurable sandboxes and SDKs for building privacy-preserving and compliant training pipelines across diverse model architectures and data types.

What’s new in DEPA-Training?

Graphical user interface

We’ve introduced an interactive GUI that enables users to explore, configure, and execute DEPA-Training scenarios end to end. The application automatically discovers available scenarios in the repository and provides an intuitive interface to run them — eliminating the need for command-line interaction. A similar GUI workflow is also provided for contract signing.

Scenarios you can try out today

To bring DEPA-Training to life, we showcase a diverse set of scenarios that demonstrate what’s possible in practice. These examples illustrate pathways toward solving larger global challenges and span multiple data modalities (e.g., tabular, images), model paradigms (e.g., classical ML, MLPs, CNNs), and prediction tasks (e.g., regression, classification, image segmentation).

Disease Surveillance Modeling

Pandemics don’t wait. Timely, accurate data can save millions of lives. Yet most infection data is scattered, siloed, and too sensitive to share. With differential privacy, institutions can securely pool data to track virus spread, map risk patterns, and test interventions — powering real-time, data-driven epidemic response.

Example: COVID-19 scenario

Medical Image Modeling

From cancer to cardiovascular disease, from neurology to rare disorders — modern medicine increasingly depends on imaging. Yet medical images are among the hardest datasets to share, trapped in hospital silos and governed by strict privacy laws. DEPA makes it possible to combine imaging data across borders and institutions, unlocking AI models that are more accurate, generalizable, and equitable. This accelerates breakthroughs in diagnostics, improves treatment planning, and addresses one of healthcare’s biggest global challenges: scaling precision medicine while safeguarding patient trust.

Example: BraTS scenario 

Financial Credit Risk Modeling

Access to fair credit fuels economic growth, but risk assessment is often limited by partial data. By safely combining insights across financial institutions, DEPA enables more accurate credit scoring, reduces defaults, and strengthens financial stability — empowering individuals and businesses alike with better access to capital.

Example: Credit Risk scenario

Build your own Scenarios

A new step-by-step guide walks you through building and running your own DEPA-Training scenarios — making it easy to rapidly prototype and iterate with training use-cases of your own.

Currently, DEPA-Training supports the following training frameworks, libraries and file formats (more will be included soon):

  • Frameworks: PyTorch, Scikit‑Learn, XGBoost (LLM Finetuning to be added soon!)
  • Libraries: Opacus, PySpark, Pandas (HuggingFace support coming soon!)
  • Formats: ONNX, Safetensors, Parquet, CSV, HDF5, PNG (No pickle-based formats for security reasons)

What’s in it for the ecosystem?

DEPA-Training democratizes responsible data sharing and model training for all!

  • Enterprises & Startups → Unlock the value of private data to build smarter products and services, while remaining compliant to data laws. Collaborate across organizations to create solutions that no single dataset could power.
  • Research Institutions → Pool data at scale to tackle grand challenges, drive scientific discovery, and advance knowledge for the public good.
  • Policy & Legal Experts → Shape the future of data governance by operationalizing privacy, consent, purpose limitation, and accountability in practice.
  • Builders & researchers → Join us in co-creating this framework!

Get started

👉 Get your hands dirty: DEPA‑Training on GitHub 🛠️

👉 Explore the documentation: DEPA.World 📜
👉 Watch the Open Houses: YouTube Playlist 🎬

👉 Think big: What challenges has data privacy kept off-limits? What data has felt forever inaccessible? With DEPA-Training, those doors may finally open. 💡

Interested in contributing to DEPA? Join our group of no-greed no-glory volunteers! Apply here

Please note: The blog post is authored by our volunteers, Sarang Galada, Dr. Shyam Sundaram, Kapil Vaswani and Pavan kumar Adukuri

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Privacy in the Age of AI: New Frameworks for Data Collaboration-Part-2

This is a two part blog series. The following is the second part.

In Part 1, we traced how data collaborations are being reimagined, and laid out the conceptual foundations. From redefining consent through the Account Aggregator framework, to recognizing the limits of consent. We explored how privacy-preserving frameworks like differential privacy protect individuals even when models are built from data; how electronic contracts replace slow, manual agreements with enforceable digital rules; and how confidential clean rooms combine secure hardware and privacy guarantees to enable computation without revealing raw data.

In Part 2, we explore how these building blocks come together in practice.

The Connective Tissue: Data Collabs

Technology alone cannot guarantee privacy, fairness, or effective collaboration. Data-sharing ecosystems need institutional scaffolding — entities that can operationalize trust, manage relationships, and abstract away complexity for participants.

This is where Data Collaboratives (or Data Collabs for short) come in.

A Data Collab isn’t a regulator or a government body. Rather, it is a facilitator organization — a neutral yet entrepreneurial entity that enables, orchestrates, and sustains data collaborations using the DEPA Framework behind the scenes, following its standards and processes set by trusted bodies like an Self-Regulatory Organization (SRO) and a Technology Standards Organization (TSO).

You can think of a Data Collab as the connective tissue of a data ecosystem — linking data providers, data consumers, and service providers.

In practice, a Data Collab:

  1. Provides tools and interfaces for participants to register, onboard, sign electronic contracts, and set up secure collaboration environments such as CCRs.
  2. Signs agreements with data providers to clean, prepare, and catalogue datasets so that they can be safely shared with authorized data consumers.
  3. Manages the flow of value — usually collecting payments from data consumers and distributing them fairly to data providers, while covering operational costs.
  4. Assumes accountability for ensuring that all interactions, permissions, and computations are compliant with the DEPA rules and contractual terms.
  5. Adds value beyond infrastructure — offering domain expertise, workflow design, governance and audit support — streamlining data collaborations.

Data Collabs will likely take different forms depending on the domain they serve. For example, some might focus on oncology research, others on financial fraud detection or climate-risk modeling. Each field has its own kinds of data, privacy rules, and ways of working — so it is natural for Data Collabs to specialize.

Because running these collaborations requires significant operational and technical effort, most Data Collabs will probably be for-profit enterprises. At the same time, because they operate on open, interoperable digital public infrastructure like DEPA, they are not monopolistic platforms. Instead, they enable a competitive marketplace where multiple Data Collabs can coexist, offering participants better choices, fairer pricing, and higher-quality services.

In this way, Data Collabs create a persistent institutional layer for responsible data use; enabling long-term, multi-party cooperation that would be impractical to coordinate through ad hoc agreements.

A real-world example: Accelerating Drug Discovery

Imagine three pharmaceutical companies, each developing treatments for the same rare disease. Each has conducted clinical trials with a few hundred patients — but individually, none has enough data in quantity, diversity, or parameter richness to train a robust predictive model of treatment response. 

Much like pieces of a puzzle, valuable insights often emerge only when data from different sources fit together — yet no single party should hold or see the entire picture.

If these companies could combine their datasets, and enrich them with other sources like gene expression profiles, cell imaging results, or public molecular databases, they could uncover deeper patterns and dramatically speed up drug discovery.

But three major barriers stand in their way:

  1. Competitive concerns: Each company treats its clinical data as proprietary and doesn’t want to reveal it to others.
  2. Privacy regulations: Patients gave consent only to the company that ran their trial — not to share data across firms.
  3. Practical limits: Many patients can’t be re-contacted to renew consent, making manual legal processes infeasible.

This is where the DEPA Framework fits in. Here’s how it would work:

A Data Collab is formed for long-term drug discovery collaborations. It signs electronic contracts with each company, defining rights, responsibilities, and permitted use of data. It handles registration, onboarding, and compliance checks through standardized interfaces.

Electronic contracts set out the exact terms of collaboration — specifying each party’s role, the artefacts they contribute, and the rules that govern privacy, usage, and value-sharing.

Each company uploads its encrypted trial data or model into a Confidential Clean Room. Data inside the CCR is decrypted only after checks confirm that all security and compliance conditions are met.

Data is programmatically joined and enriched within the CCR, followed by AI model training using privacy-enhancing techniques like differential privacy, which appropriately bound the chance of re-identifying patients.

Only the final trained model and its accompanying logs — never the underlying data — leave the CCR. The model can be decrypted solely by the authorized data consumer(s) (i.e. the modellers), protecting their trade secrets.

Auditors can review logs and trace the provenance of all artefacts at any time — via the DEPA AI Chain — to verify compliance and resolve disputes.

This framework delivers several benefits for all concerned stakeholders:

  • For society: Promising treatments reach patients faster, while a reusable governance and technology blueprint emerges for future biomedical collaborations. 
  • For the economy: A new data-driven economy is unlocked, enabling novel business interactions and boosting meaningful economic activity.
  • For companies: They can innovate together without exposing trade secrets or breaking regulatory rules, expanding what’s possible in research and development.
  • For regulators and auditors: Every transaction leaves a verifiable trail, simplifying oversight and boosting trust in the ecosystem.

Summing up

India’s journey toward responsible data use has been progressive and layered.

  • It began with the Account Aggregator framework — making consent Open, Revocable, Granular, Auditable, Notifying and Secure (ORGANS principle).
  • For model training and analytics, Privacy-Enhancing Technologies (PETs) — such as Differential Privacy — introduce mechanisms like the privacy budget to safeguard individuals while enabling learning.
  • To make collaboration faster and more reliable, Electronic Contracts replace traditional paper/PDF agreements with machine-readable, enforceable commitments — cutting through the friction of slow legal processes.
  • Confidential Clean Rooms (CCRs) operationalize these safeguards — enabling computation on sensitive data.
  • Finally, Data Collaboratives weave all these elements together — creating institutional and economic frameworks that make responsible, long-term data collaboration practical and sustainable.

This is the next frontier of Digital Public Infrastructure for AI — proving that protection and innovation are not opposites. With the right frameworks, we can have both.

Read Part 1: Privacy in the Age of AI: New Frameworks for Data Collaboration-Part-1

Please note: The blog post is authored by our volunteers, Hari Subramanian and Sarang Galada

For more information, please visit: https://depa.world/

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Privacy in the Age of AI: New Frameworks for Data Collaboration-Part-1

This is a two part blog series. The following is the first part.

Every day, we generate vast amounts of digital data — withdrawing cash, visiting doctors, ordering groceries, using various mobile apps. These data trails have the potential to streamline services, personalize experiences, and drive breakthroughs in fields from medicine to finance. Yet they also carry risks: unfair profiling, intrusive targeting, and exposure of sensitive personal information.

This presents a fundamental challenge: How can we harness the value of data while preserving individual privacy?

Understanding Privacy

In the age of AI, privacy violations no longer just expose personal information. They erode autonomy and tilt power toward those who control data and algorithms. As AI systems harvest behavioral cues, digital footprints, and social networks, people lose control, not just over their information, but also over how they are profiled and influenced. This enables subtle yet pervasive forms of coercion, from tailored manipulation of choices to algorithmic exclusion from opportunities.

At scale, such surveillance dynamics erode trust and weaken democratic agency. In this era, privacy is not merely about secrecy, it is a precondition for freedom, dignity and meaningful participation in society.

Privacy is often mistaken for confidentiality, but it’s not simply about hiding information. Privacy is the property of not being able to identify individuals from the signals they produce. Confidentiality, on the other hand, is about limiting access to those signals in the first place. To protect privacy and confidentiality while respecting individual autonomy, we need strong control mechanisms that let people decide what data is shared, with whom, for what purpose, and for how long.

And privacy isn’t a one-time setting. Data moves through a lifecycle — it is collected, used, stored, reused, and eventually deleted. These protections must hold at every stage, or they are lost.

The Mechanics of Consent

Today, consent remains the most common mechanism for privacy — the basic control primitive intended to let people decide how their data is collected, shared, and used. The concept of consent actually predates the digital era — it began in a paper-based world, where signatures and written permissions served as the primary means of authorizing data use. 

It is important to distinguish between two kinds of consent:

  1. Consent to collect data – allowing an entity to initially gather your data (for example, an app accessing your camera).
  2. Consent to share data – granting permission for that data to be used or passed on for a specific purpose (for example, a bank sharing your salary details with a loan underwriter).

Our focus in this article is on consent to share data, since that is where both the greatest privacy challenges and the most meaningful opportunities for value creation lie.

Here is the problem with how consent is currently implemented today. Under frameworks like GDPR, consent has been defined as a very coarse-grained and blunt artifact. The same entity collects your data, gathers your consent, and enforces the rules around its use. For individuals, this typically means an all-or-nothing choice — share everything or nothing at all. And for innovators, it stifles the ability to responsibly explore new uses of data.

India’s Innovation: Unbundling Consent

When India designed its Account Aggregator system for financial data sharing, it chose a different path. Consent to share data was unbundled into two parts:

  • Collect consent: Managed by trusted intermediaries called Account Aggregators.
  • Enforce consent: Managed downstream by Financial Information Users (like banks or wealth advisors), under ecosystem oversight.

https://sahamati.org.in/what-is-account-aggregator/

At the heart of this design lies a set of principles that make consent Open, Revocable, Granular, Auditable, Notifying, and Secure — or ORGANS for short.

The Account Aggregator (AA) framework became the first manifestation of DEPA — the Data Empowerment and Protection Architecture. It is now India’s go-to model for user-consented data sharing between institutions, especially for straightforward data transfers and simple inference tasks.

Where Consent Reaches Its Limits

Consent works well for inferences — one-time decisions like a bank checking your last six months of transactions to approve a loan. Yet, in practice, consent has well-known limits. People are asked to grant permission repeatedly, often through long, opaque terms they don’t fully understand, leading to consent fatigue and a loss of meaningful control.

These limitations become clearer when we move from individual decisions to model training and large-scale analytics, where algorithms learn patterns from millions of records. Seeking or managing consent at that scale is neither practical nor effective. 

What’s worse is that models can sometimes memorize sensitive data and inadvertently reveal it later. This highlights the need for new, complementary control primitives that uphold privacy and accountability even when explicit consent isn’t feasible.

Attempts at de-identification — the process of removing or masking identifiers to anonymize data – have significant limitations in practice. Although anonymization is meant to ensure that individuals cannot be re-identified, de-identification techniques are often reversible when datasets are combined with external information. As a result, such approaches offer only weak privacy guarantees, and numerous cases have shown how easily supposedly “anonymous” data can be linked back to individuals.

Privacy-preserving Algorithms: A New Control Primitive for Training and Analytics

To address these limits, a new class of algorithms has emerged under the broad umbrella of Privacy-Enhancing Technologies (PETs). Let us call these privacy-preserving algorithms, to differentiate them from other classes of PETs. They provide a spectrum of technical safeguards that preserve privacy while still enabling useful computation and collaboration on sensitive data.

Among these, Differential Privacy (DP), a mathematical framework for preserving individual privacy in datasets, stands out as a powerful privacy primitive for model training and data analysis.

The key idea: DP adds carefully calibrated noise to queries or model updates so that the results are statistically indistinguishable whether or not any single individual’s data is included. This ensures that nothing specific about an individual can be reliably inferred.

To make this guarantee rigorous, DP introduces the concept of a privacy budget (often represented by the parameters epsilon ε and delta δ):

  • Each query or training step “spends” some of this budget.
  • With more queries or training epochs, the cumulative privacy loss increases.
  • Once the budget is exhausted, no further queries or training is allowed, keeping the risk of re-identification mathematically bounded.

Think of this as a quantitative accounting system for privacy loss. Note, however, that DP comes with a utility tradeoff: adding calibrated noise can reduce model accuracy or data usefulness. Hence, depending on the use-case, the right privacy controls may be achieved through other privacy-preserving algorithms, or a combination thereof.

Electronic Contracts: Digitizing Trust

While privacy-preserving computation enables data to be used securely, participants still need clear agreements defining who may use it, for what purpose, or under what conditions. For such collaborations to function effectively, there must be a well-defined and enforceable contractual framework that specifies each party’s rights, obligations, and permissions.

The need for such a framework becomes even more pressing as organizations seek to unlock real value from data. No single dataset is enough; the most meaningful insights arise when information from multiple sources — hospitals, banks, labs, startups, or agencies — can be combined and analyzed responsibly. Yet each participant brings its own rules, contracts, and compliance obligations, creating a patchwork of agreements that are difficult to align.

Traditionally, contracts are legal documents — PDFs or paper agreements — written in human language, interpreted by lawyers, and enforced by institutions. They work well when a few parties are involved, but in modern data collaborations, this model quickly breaks down.

Today, every new collaboration means drafting, signing, and managing a maze of separate legal agreements, often in different formats, scattered across systems, and maintained by hand. With every participant added, the web of contracts grows bulkier, making coordination slow, expensive and error-prone. Every change or dispute requires human intervention and can take weeks or months to resolve.

This contractual friction has long been the viscous drag holding back scalable, compliant data collaboration. Not because trust is missing, but because it is buried under paperwork.

Electronic contracts transform this equation. They are machine-readable, digitally signed, and executable agreements that translate legal promises into enforceable code. Instead of being static documents, they are active digital objects that the DEPA orchestration layer can interpret and act upon — automatically initiating workflows, enforcing permissions, and ensuring compliance.

In effect, electronic contracts bridge law and computation.  They enable trust, automation, and accountability at digital speed, replacing manual paperwork with a system that can verify, execute, and audit commitments in real time.

Confidential Clean Rooms (CCR)

To operationalize the above elements, we need infrastructure that embeds privacy and compliance mechanisms by design, while also supporting diverse collaboration modalities — from data analytics and model training to various forms of inference.

That’s where Confidential Clean Rooms (CCRs) come in. A CCR is a secure computing environment that allows organizations to collaborate on data without ever sharing it in plain form. You can think of it as a locked, monitored laboratory where data from multiple parties can be brought together for analysis — yet no participant, not even the operator of the lab, can peek inside.

At the heart of every CCR is Confidential Computing — a technology that uses Trusted Execution Environments (TEEs) built into modern processors.  When data enters a TEE, it is encrypted and isolated from the rest of the system, ensuring that even cloud providers or system administrators cannot access it. Computations run inside this protected enclave, and only verified results can leave. Each TEE also produces a cryptographic attestation, a proof that the computation was executed correctly and under the agreed conditions.

https://depa.world/training/architecture

On their own, CCRs provide secure execution. But when combined with other DEPA primitives..

  1. Electronic Contracts, which specify who can use what data for what purpose, and
  2. Privacy-preserving algorithms, which provide mathematical controls about what information can or cannot leak,

..they form a complete privacy-preserving data-sharing stack.

In essence, Confidential Clean Rooms (CCRs) enable confidential, techno-legal, and privacy-preserving computation on data. They make it possible to conduct large-scale data inference, analytics and modelling responsibly, without transferring raw data to any third party, and thereby eliminating the need for consent specifically for data sharing.

But technology alone doesn’t build ecosystems. Who brings this framework to life, abstracting away its complexity for everyday organizations? How might it help us confront our most urgent global challenges — in health, climate and finance? And how could it unlock entirely new kinds of enterprises, fueling a vibrant and responsible data economy for the Intelligence Age?

Data Collabs!

Read Part 2: Privacy in the Age of AI: New Frameworks for Data Collaboration-Part-2

Please note: The blog post is authored by our volunteers, Hari Subramanian and Sarang Galada

For more information, please visit: https://depa.world/

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Open House on DPI for AI #4: Why India is best suited to be the breeding ground for AI innovation!

This is the 4th blog in a series of blogs describing and signifying the importance of DPI for AI, a privacy-preserving techno-legal framework for AI data collaboration. Readers are encouraged to first go over the earlier blogs for better understanding and continuity. 

We are at the cusp of history with regard to how AI advancements are unfolding and the potential to build a man-machine society of the future economically, socially, and politically. There is a great opportunity to understand and deliver on potentially breakthrough business and societal use cases while developing and advancing foundational capabilities that can adapt to new ideas and challenges in the future. The major startups in Silicon Valley and big techs are focused first on bringing the advancements of AI to first-world problems – optimized and trained for their contexts. However, we know that first world’s solutions may not work in diverse and unstructured contexts in the rest of the world – may not even for all sections of the developed world.

Let’s address the elephant in the room – what are the critical ingredients that an AI ecosystem needs to succeed –  Data, enabling regulatory framework, talent, computing, capital, and a large market. In this open house

we make a case that India is the place that excels in all these dimensions, making it literally a no-brainer whether you are an investor, a researcher, an AI startup, or a product company to come and do it in India for your own success. 

India has one of the most vibrant, diverse, and eager markets in the world, making it a treasure chest of diverse data at scale, which is vital for AI models. While much of this data happens to be proprietary, the DPI for AI data collaboration framework makes it available in an easy and privacy-preserving way to innovators in India. Literally, no other country has such a scale and game plan for training data. One may ask that diversity and scale are indeed India’s strengths but where is the data? Isn’t most of our data with the US-based platforms? In this context, there are three types of Data: 

a. Public Data,
b. Non-Personal Data (NPD), and
c. Proprietary Datasets.

Let’s look at health. India has far more proprietary datasets than the US. It is just frozen in the current setup. Unfreezing this will give us a play in AI. This is exactly what DPI for AI is doing – in a privacy-preserving manner. In the US, health data platforms like those of Apple and Google are entering into agreements with big hospital chains – to supplement their user health data that comes from wearables. How do we better that? This is the US Big Tech-oriented approach – not exactly an ecosystem approach. Democratic unfreezing of health data with hospitals is the key today. DPI for AI would do that even for all – small or big, developers or researchers! We have continental-scale data with more diversity than any other nation. We need a unique way to unlock them to enable the entire ecosystem, not just big corporations. If we can do that, and we think we can via DPI for AI, we will have AI winners from India.

Combine this with India’s forward looking regulatory thought process that balances Regulation for AI and Regulation of AI in a unique way that encourages innovation without compromising on individual privacy and other potential harms of the technology. The diversity and scale of the Indian market act like a forcing function for innovators to think of robustness, safety, and efficiency from the very start which is critical for the innovations in AI to actually result in financial and societal benefits at scale. There are more engineers and scientists of Indian origin who are both creating AI models or developing innovative applications around AI models. Given our demographic dividend, this is one of our strengths for decades to come. Capital and Compute are clearly not our strong points, but capital literally follows the opportunity. Given India’s position of strength on data, regulation, market, and talent, capital is finding its way to India!

So, what are you all waiting for? India welcomes you with continental scale data with a lightweight but safe regulatory regime and talent like no place else to come build, invest, and innovate in India. India has done it in the past in various sectors, and it is strongly positioned to do it again in AI. Let’s do this together. We are just getting started, and, as always, are very eager for your feedback, suggestions, and participation in this journey!

Please share your feedback here
For more information, please visit depa.world

Please note: The blog post is authored by our volunteers, Sharad Sharma, Gaurav Aggarwal, Umakant Soni, and Sunu Engineer

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Open House on DEPA Training #3: The Regulatory and Legal Aspects

This is the third in a series of blogs describing  the structure and importance of Digital Public Infrastructure for Artificial Intelligence (DPI for AI), a privacy-preserving techno-legal framework for data and AI model building  collaborations. Readers are encouraged to go over the first and second blogs for better understanding and continuity.

Open House on DEPA Training #1

Open House on DEPA Training #2: DPI to Unfreeze Data Markets. Let’s Make India an AI Nation!

The techno-legal framework of DEPA, elaborated upon in the earlier blogs, provides the foundations. From multiple discussions and history, it is clear that building and growing a vibrant AI economy that can create a product nation in India, requires a regulatory framework. This regulatory structure will serve as the legal partner to the technology aspect and work hand in hand with it. Upon this reliable techno-legal foundation will the ecosystem and global product companies from India be materialized.

‘Data Empowerment And Protection Architecture’ – or DEPA’s – worldview of ‘regulation for AI’, rather than the more conventional ‘Regulation of AI’ espoused by US, EU and so on sets DEPA apart and drives India towards an AI product nation with a global footprint.

How does one envisage the form and function of ‘Regulation for AI’?  In this open house, we have a dialog between technology and legal sides of the approach to explain the significant facets.

In a nutshell, ‘Regulation for AI’ will focus on 

  • what standards the AI models need to adhere to
  • define a lightweight but foolproof path for getting there for startups as well as the big players 
  • provide an environment which deals with many of the compliance and safety aspects ab initio 
  • define ways to remove hurdles from the innovator’s paths

In contrast, ‘Regulation of AI’ deals with what AI models cannot be and do and the tests and conditions that they have to pass depending on the risk classes that they are placed into. This is akin to certification processes in many fields such as pharma, transportation and so on which impose heavy cost burdens, especially on new innovators. For instance, many pharma companies which develop potentially good drug candidates run out of steam trying to meet the clinical trial conditions. Very often they are unable to find a valid and sizeable sample population to test their products as a part of the mandatory certification process. 

The current standards in the new Regulation of AI in the US, EU and so on leave many aspects such as the risk model classification process undefined, leading to regulatory uncertainty. This also works against investment driven innovation and consequent growth of the ecosystem in multiple ways.

The path to value both for the economy and the users, lies in the power of the data being projected into the universe of applications. These applications will be powered by the AI models in addition to other algorithmic engines. The earlier blogs already addressed the need and the way for data to make their way into models. 

For the models to exhibit their power, we must make sure they are reliable and used widely. This requires the AI models be accessible and available and most importantly, ‘do no harm’ when they are applied, through mistakes, misuse or malfeasance.  In addition to this, humans or their agents must not be allowed to harm the markets and users through monopoly control of the AI models. Large scale monopolistic control of these models which have global use and relevance can lead to situations which are beyond national or international legislation to control or curb. 

In the DEPA model, this benign, and in most ways, benevolent environment is created by a concinnous combination of technology and legal principles. Having analyzed the technological aspects of data privacy in the earlier blogs in this chain, here we will talk about the regulations implemented via a Self-Regulatory Organization – the SRO.  

Though not fully fleshed out, the SRO provides functions such as registration and roles to participants such as TDP (Training Data Provider), TDC (Training Data Consumer) and CCRP (Confidential Clean Room Provider). Many of these functions have been implemented in part to support the tech stack that we have released with respect to the CCR (Ref: DEPA Open House #1). This tech stack currently supports registration and allows the interactions between participants to be mediated via electronic contracts (the technological counterpart of legal contracts). 

The technology that validates the models through pre-deployment analysis based on complex adaptive system models is under development and is based on diverse research efforts across the world. This technology is designed for measuring the positive and negative impact of use of these models on societies at small and large scale and in short and long timescales.   

‘Complex adaptive system models’ are dynamic models which can capture agents with their state information and the multiple feedback loops which determine the changes in the system at different scales, sometimes simultaneously. The large number of components and the many kinds of feedback loops with their dynamic nature are what make these models complex and adaptive. These models, while still in their infancy in many ways, are critical to the question of understanding the AI models’ impact on societies. 

The SRO guides and supports the ecosystem players in building and deploying their models in a safe and secure way with lightweight regulatory ceilings so that large product companies in many fields like finance, healthcare, and education can grow and reach a happy consumer base. This is key to growing the ecosystem and connecting it to other parts of the India stack. 

We envisage leveraging the current legal system in terms of the different Acts (DPDP, IT Act, Copyright etc.) and models of Data Protection through CDO ( Chief Data Office) and CGO ( Grievance Office) in companies in India in defining the SRO’s role and features further.

The regulatory model also looks at the question of data ownership and copyright issues, especially in the context of Generative AI. We require large foundation models independent of the ‘Big Tech’ to fight potential monopolies. These models should be reflective of the local diversity to serve as reliable engines in the context of India. We need these models built and deployed locally, to be able to play a role as a product nation without being subverted or subjugated in our cyberspace strategies. 

To light up the AI sky with these many ‘fireflies’ in different parts of India, infrastructure for compute as well as market access is needed. The SRO creates policies that are not restrictive or protective but promotes participation and value realization. The data players, compute providers, market creators and users need to be able to play with each other in a safe space. Sufficient protection of copyright and creative invention will be provided via existing IP law to incentivize participation while not restricting to the point of killing innovation – this is the balance that the regulatory framework of SRO strives to reach. 

Drawing upon ideas of risk-based categorization of models (such as in the EU AI Act) and regulatory models (including punitive and compensatory measures) proportional to them, the models in India Stack will be easily compatible with international standards, as well as a universal or global standard, should an organization such as a UN agency define it. This makes global market reach of   AI models and products built in India, an easier target to achieve. 

We conjecture that these different aspects of DEPA will release the data from its silos. AI models will proliferate with multiple players profiting from infrastructure, model building, and exporting them to the world. Many applications will be built which will be used both in India (as part of the India Stack) and the world. It is through these models and applications that the latent potential and knowledge in the vast stores of data in India will be realized.

Please share your feedback here

For more information, please visit depa.world

Please note: The blog post is authored by our volunteers, Antara Vats, Vibhav Mithal and Sunu Engineer

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Open House on DEPA Training #2: DPI to Unfreeze Data Markets. Let’s Make India an AI Nation!

This is the 2nd blog in a series of blogs describing and signifying the importance of DPI for AI, a privacy-preserving techno-legal framework for AI data collaboration. Readers are encouraged to first go over the 1st blog for better understanding and continuity.

What is unique about the techno-legal framework in DPI for AI is that it allows for data collaboration without compromising on data privacy. Now let’s put this in perspective of Indian enterprises and users. This framework can potentially revolutionize the entire ecosystem to slingshot India towards an AI product nation where we are not just using AI models developed within India but exporting the same. What is the biggest roadblock in this dream? In this open house (https://bit.ly/DEPA-2), we make a case that privileged access to data from Indian contexts is not only necessary to develop AI-based systems that are much more relatable to Indians but in fact, gives Indian innovators a distinct advantage over much larger and better funded big tech companies from the west.

Let’s get started. Clearly, there is a race to build larger and larger AI models these days trained on as much training data as possible. Most training data used in the models is publicly available on the web. Given that Indian enterprises are quite behind in this race, it is unlikely that we will catch up by simply following their footsteps. But what many folks outside of AI research circles often miss is that there has been credible research that shows that access to even relatively small amounts of contextual data can drastically reduce the data and compute requirements to achieve the same level of performance.

This sounds great, right, but (there is always a 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 for AI. The three core concepts of DPI for AI ensure that this data sitting in silos can be seamlessly (thanks to digital contracts) and democratically shared with innovators around India in a privacy-preserving manner (thanks to differential privacy). The innovators also do not need to worry one bit about the confidentiality of their IP (thanks to confidential computing). The techno-legal framework makes it super easy for anyone to abide by the privacy regulations without sweat. This will keep them safe from future litigations as long as they follow easy-to-follow guidelines provided in the framework. This is what we refer to as the unfreezing of data markets in this Open House. This unfreezing 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 moment to leapfrog in the field of AI as we have done in so many domains (payments, identity, internet, etc.). Given the enormity of the goal and the need to get it right, we seek participation from folks from varied expertise and backgrounds. Please share your feedback here

For more information, please visit depa.world

Please note: The blog post is authored by our volunteers, Hari Subramanian and Gaurav Aggarwal.

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Introducing DEPA for Training: DPI for Responsible AI

In the last decade, we’ve seen an extraordinary explosion in the volume of data that we, as a species, generate. The possibilities that this data-driven era unlocks are mind-boggling. Large language models, trained on vast datasets, are already capable of performing a wide array of tasks, from text completion to image generation and understanding. The potential applications of AI, especially for societal problems, are limitless. However, lurking in the shadows are significant concerns such as security and privacy, abuse and mis-information, fairness and bias.

These concerns have led to stringent data protection laws worldwide, such as the European Union’s General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA), and the European AI Act. India has recently joined this global privacy protection movement with the Data Protection and Privacy Act of 2023 (DPDP Act). These laws emphasize the importance of individuals’ right to privacy and the need for real-time, granular, and specific consent when sharing personal data.

In parallel with privacy laws, India has also adopted a techno-legal approach for data sharing, led by the Data Empowerment and Protection Architecture (DEPA). This new-age digital infrastructure introduces a streamlined and compliant approach to consent-driven data sharing.

Today, we are taking the next step in this journey by extending DEPA to support training of AI models in accordance with responsible AI principles. This new digital public infrastructure, which we call DEPA for Training, is designed to address critical scenarios such as detecting fraud using datasets from multiple banks, helping with tracking and diagnosis of diseases, all without compromising the privacy of data principals.

DEPA for Training is founded on three core concepts, digital contracts, confidential clean rooms, and differential privacy. Digital contracts backed by transparent contract services make it simpler for organizations to share datasets and collaborate by recording data sharing agreements transparently. Confidential clean rooms ensure data security and privacy by processing datasets and training models in hardware protected secure environments. Differential privacy further fortifies this approach, allowing AI models to learn from data without risking individuals’ privacy. You can find more details how these concepts come together to create an open and fair ecosystem at https://depa.world.

DEPA for Training represents the first step towards a more responsible and secure AI landscape, where data privacy and technological advancement can thrive side by side. We believe that collaboration and feedback from experts, stakeholders, and the wider community are essential in shaping the future of this approach. Please share your feedback here

For more information, please visit depa.world

Please note: The blog post is authored by our volunteer, Kapil Vaswani

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Policy Hacks Session on GDPR & DEPA

Here are concerns and curiosity about European Union General Data Protection Regime (GDPR) and there is a related issue in India being covered under Data Empowerment and Protection Architecture (DEPA) layer of India Stack being vigorously followed at iSPIRT.

iSPIRT organised a Policy Hacks session on these issues with Supratim Chakraborty (Data Privacy and Protection expert from Khaitan & Co.), Sanjay Khan Nagra (Core Volunteer at iSPIRT and M&A / corporate expert from Khaitan & Co) and Siddharth Shetty (Leading the DEPA initiative at iSPIRT).

Sanjay Khan interacted with both Siddharth and Supratim posing questions on behalf of Industry.

A video of the discussion is posted here below. Also, the main text of discussion is given below. We recommend to watch and listen to the video.

GDPR essentially is a regulation in EU law on data protection and privacy for all individuals within the European Union. It also addresses the export of personal data outside the EU.

Since it affects all companies having any business to consumer/people/individual interface in European Union, it will be important to understand this legal framework that sets guidelines for the collection and processing of personal information of individuals within the European Union (EU).

Supratim mentioned in the talk that GDPR is mentioned on following main principles.

  1. Harmonize law across EU
  2. Keep pace with technological changes happening
  3. Free flow of information across EU territory
  4. To give back control to Individual about their personal data

Siddharth explained DEPA initiative of iSPIRT. He mentioned that Data Protection is as important as Data empowerment. What this means is that individual has the ability to share personal data based on one’s choice to have access to services, such as financial services, healthcare etc. DEPA deal with consent layer of India Stack.

This will help service providers like account aggregators in building a digital economy with sufficient control of privacy concerns of the data. DEPA essentially is about building systems so that individual or consumer level individual is able to share data in a protected manner with service provider for specified use, specified time etc. In a sense, it addresses the concern of privacy with the use of a technology architecture.

DEPA is being pursued India and has nothing to do with EU or other countries at present.

For more details on DEPA please use this link here http://indiastack.org/depa/

Sanjay Khan poses a relevant question if GDPR is applicable even on merely having a website that is accessible of usable from EU?

Supratim explains, GDPR applicable, if there is involvement of personal data of the Data subjects in EU. Primarily GDPR gets triggered in three cases

  1. You have an entity in EU,
  2. You are providing Goods and services to EU data subjects whether paid for or not and
  3. If you are tracking EU data subjects.

Many people come in the third category. The third category will especially apply to those websites where it is proved that EU is a target territory e.g. websites in one of the European languages, payment gateway integration to enable payments in EU currency etc.

What should one do?

Supratim, further explains that the important and toughest task is data management with respect to personal data. How it came? where all it is lying? where is it going? who can access? Once you understand this map, then it is easier to handle. For example, a mailing list may be built up based on business cards that one may have been collected in business conferences, but no one keeps a track of these sources of collections. By not being able to segregate data, one misses the opportunity of sending even legitimate mailers.

Is a data subject receives and gets annoyed with an obnoxious email in a ‘subject’ that has nothing do with the data subject, the sender of email may enter into the real problem.

Siddharth mentioned that some companies are providing product and services in EU through a local entity are shutting shops.

Supratim, mentions that taking a proper explicit and informed consent in case of email as mentioned GDPR is a much better way to handle. He emphasised the earlier point of Data mapping mentioned above, on a question by Sanjay khan. Data mapping, one has to define GDPR compliant policies.

EU data subjects have several rights, edit date, port data, erase data, restrict data etc. GDRP has to be practised with actually having these rights enabled and policies and processed rolled out around them. There is no one template of the GDPR compliant policies.

Data governance will become extremely important in GDPR context, added Siddharth. Supratim added that having a Data Protection officer or an EU representative may be required as we go along in future based upon the complexity of data and business needs.

Can it be enforced on companies sitting in India? In absence of treaties, it may not be directly enforceable on Indian companies.  However, for companies having EU linkages, it may be a top-down effect if the controller of a company is sitting there.

Sanjay asked, how about companies having US presence and doing business in EU. Supratim’s answer was yes these are the companies sitting on the fence.

How about B2B interactions? Will official emails also be treated as personal? Supratim answers yes it may. Again it has to be backed by avenues where data was collected and legitimate use. Supratim further mentions that several aspects of the law are still evolving and idea at present is to take a conservative view.

Right now it is important to start the journey of complying with GDPR, and follow the earlier raised points of data mapping, start defining policy and processes and evolve. In due course, there will be more clarity. And if you are starting a journey to comply with GDPR, you will further be ready to comply with Indian privacy law and other global legal frameworks.

“There is no denying the fact that one should start working on GDPR”, said Sanjay. “Sooner the better”, added Supratim.

We will be covering more issues on Data Protection and Privacy law in near future.

Author note and Disclaimer: PolicyHacks, and publications thereunder, are intended to provide a very basic understanding of legal/policy issues that impact Software Product Industry and the startups in the eco-system. PolicyHacks, therefore, do not necessarily set out views of subject matter experts, and should under no circumstances be substituted for legal advice, which, of course, requires a detailed analysis of the relevant fact situation and applicable laws by experts in the subject matter on the case to case basis.