Unlocking Data Privacy for Enterprise AI - Data Safe Fine-Tuning and Training

Enterprises collect vast amounts of sensitive data, from personal healthcare records to financial transactions, in order to fine-tune or train machine learning (ML) models. However, with increasing regulations such as GDPR, HIPPA, and heightened consumer expectations around privacy, leveraging this data while safeguarding sensitive information is a critical challenge for enterprises.

As a Product Manager at Ori, I’ve seen how enterprises prioritise data security when leveraging GPUs for high performance AI workloads. In this blog, we'll explore what data privacy is, why it’s crucial and discuss how one of the approaches called “Differential Privacy” works with some examples along with its benefits of integrating privacy techniques during AI model training.

Data Privacy and Why does it Matter?

Data privacy involves safeguarding personal or sensitive information so it cannot be linked to an individual, either by names or unique behavior patterns. Compliances such as GDPR and CCPA set strict guidelines on how to collect, use, and store personal data. Protecting user data shows responsibility and transparency. If data is leaked or misused, it can break trust leading to lost business and legal penalties.

When enterprises fine-tune the foundational models on sensitive datasets, such as customer logs, or medical records, there’s a risk that personal details could leak through the model’s outputs or hidden layers. 

For example, imagine you have a language model trained on support tickets, if an attacker is able to reverse-engineer it might reveal user information. The goal of “data safe fine-tuning” is to keep the model’s accuracy high while making sure no individual’s data can be exposed, even probed by sophisticated attackers.

Introduction to Differential Privacy

Differential privacy is a method for protecting individual privacy when analyzing or sharing data. It adds “noise” (randomness) into a dataset so that the machine learning model parameters do not reveal sensitive details about any specific individual in the dataset. This way, you can build a useful ML model, such as one trained on medical records without revealing anyone’s personal information.

Differential privacy helps address the risks by:

1. Data Confidentiality - If the model is shared or if certain information is leaked, it’s much less likely that exact personnel details will be revealed.
2. Compliance - Data protection regulations require organizations to show that they have taken strong necessary measures to safeguard personal information.
3. Trustworthiness - Customers are more likely to trust services that incorporate best practices.

To know more about Differential Privacy, you can check out this article.

Why Anonymization isn’t enough?

There are conventional approaches for privacy such as Anonymization. In this approach, identifiable information such as names, or phone numbers could be stripped out from a dataset or replaced with IDs. However, this often fails because:

• Even stripped data can be matched with external public records. 
• Details such as ZIP codes, or birth dates, when combined, can still reveal someone’s identity. 
• Lastly, plain anonymization doesn’t add any randomness, so skilled attackers can uncover private information.

Because of these limitations, differential privacy goes beyond anonymization, ensuring that the outcome of a model remains the same whether any single person’s data is included or not, making it much harder for attackers to isolate specific individuals.

How Differential Privacy Works?

At a high level, differential privacy (DP) adds mathematically calculated noise to the data queries or gradients used in model training. This noise ensures that presence or absence of any data record does not significantly affect the final result.

The following are the key mechanisms DP uses:

1. Laplace Mechanism (for numerical data) - It adds scaled “noise” drawn from Laplace distribution to numerical queries or model updates, helping mask individual records.
2. Exponential Mechanism (for discrete choices) - Randomly picks an output, e.g., a label or parameter, with a probability based on how “good” that output is, plus noise to mask specific data contributions.

Differential Privacy in Model Training

There are 2 important steps when training ML models while protecting privacy:

1. Noise in Gradients - Add random noise to the model’s training updates or gradients so it is harder to trace back individual data points.
2. Clipping Gradients - Set a limit (clip) on how each data point’s contribution can be, preventing any single piece of data from having influence in revealing private data.

Popular libraries like PyTorch Opacus and Tensorflow Privacy implement these techniques in a straightforward way.

Here is an conceptual snippet using Opacus:

• noise_multiplier is how much noise you add. The higher the noise, the stronger the privacy guarantee, but it can reduce model accuracy.
• max_grad_norm ensures no single data point overly influences the model update.

Benefits

1. Regulatory compliance: Differential privacy helps you comply with regulations by mathematically guaranteeing that individual data stays protected.
2. Builds trust: Proactive privacy measures strengthen your brand’s reputation and reassure customers..
3. Competitive edge: Being a step ahead on compliance can set you apart in the market.
4. Easy to scale: Once set up, privacy-focused processes can be reused across multiple projects without a complete overhaul.
5. Future-ready: Once set up, privacy-focused processes can be reused across multiple projects without a complete overhaul.

By adding a bit of noise and controlling how the model sees data, Differential Privacy can make it much harder for anyone to discover personal details. For enterprises that want to tap into privacy rules and keep user trust, training with differential privacy is extremely helpful.

Chart your own AI reality with Ori

Ori Global Cloud provides flexible infrastructure for any team, model, and scale. Backed by top-tier GPUs, performant storage, and AI-ready networking, Ori enables growing AI businesses and enterprises to deploy their AI models and applications in a variety of ways:

• Deploy Private Clouds for flexible, large-scale GPU clusters.
• Operate Inference Endpoints effortlessly at any scale.
• Leverage GPU Instances as on-demand virtual machines.
• Scale GPU Clusters for training and inference.
• Manage AI workloads on Serverless Kubernetes without infrastructure overhead.