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Challenges with Implementing and Using Inference Models

Executive Summary

As machine learning and artificial intelligence become integrated into organizational and government processes and strategies, their use across various sectors has become much more widespread. However, as their adoption grows, so too do the privacy concerns associated with utilizing vast amounts of data for model training and inferencing. Navigating the balance between leveraging data for predictive insights and safeguarding sensitive information has become a prominent challenge for organizations and governments. This introduces a pressing need for solutions that can protect data privacy without compromising the utility and effectiveness of machine learning models in real-world applications. In this article, we present a solution that upholds zero trust protections of both the data and the model, enabling model users and data vendors to use and share the value of the dataset without compromising any sensitive information.

What is Model Inference in Machine Learning?

Artificial intelligence (AI) and machine learning (ML) continue to drive change and enable new possibilities across various applications and industries. 

One of the main components of this technology is the use of inferencing which is a process where trained models are used to make predictions or derive actionable insights from new data. Essentially, these generative models use the patterns learned from existing raw observational data during the training phase, which is when a developer provides a machine learning model with a curated dataset, to find the highest probability outcomes and make educated guesses on a data source.

However, there are many challenges when building, implementing, and using inferencing models, including data privacy concerns, technical complexities, and personalization challenges. Duality’s Secure Collaborative AI solution enables organizations to access and analyze sensitive data to train machine learning models or run real-time inferences on decentralized data points without revealing sensitive information, PII, or IP. This approach overcomes the traditional hurdles related to data privacy and enables the full potential of machine learning inference models.

How Can Inference Models Be Used?

To truly comprehend the challenges of implementing and using these complex models, let’s examine a hypothetical yet commonly encountered scenario:

Assume a financial institution wants to better prevent fraudulent activities and improve its customer service quality. It finds an inference model developed by a leading tech firm that predicts fraudulent behavior. However, two significant roadblocks prevent the successful implementation of the model:

1. The tech firm is unwilling to provide its model as it encapsulates years of deep learning insights and historical data, which contains crucial intellectual property, and sharing the model can risk a potential competitive disadvantage.

2. The financial institution houses millions of transactions from its customers which contain sensitive data that it cannot risk exposing to any external entity due to privacy and security laws.

This leaves the financial institution in a predicament as they’ve recognized the value of a powerful tool that can help boost their services but are hindered by data privacy and intellectual property concerns.

The financial institution is reluctant to share its large datasets with the tech firm because it contains millions of sensitive customer transactions. They are concerned for customer privacy as well as the legal implications of using this sensitive data. This is further complicated by the tech firm’s hesitation to share its proprietary model which hinders the tech firms and the financial institution’s ability to securely collaborate on the model as they are both worried about their data privacy. 

Without a solution that addresses both sides of the data privacy concern, there is no way to utilize the model or the data. 

What’s the Solution?

It’s common scenarios like this where our secure collaborative AI solution can prove vital.

With secure collaborative AI, organizations, and governments can securely collaborate on data and trained models. By leveraging advanced privacy-enhancing technologies (PETs), AI collaboration ensures that input data privacy is protected while safeguarding the model’s intellectual property. This facilitates model users to personalize and fine-tune models to their specific datasets, enabling the full potential of collaboration while mitigating risks, increasing the amount of available data, and ensuring compliance with stringent regulations.

This means that the financial institution can retain control over its sensitive customer data while still benefiting from the insights provided by the tech firm’s inference model. Additionally, SCAI allows for the personalization and fine-tuning of models to specific datasets, enabling effective collaboration without compromising data privacy or security. This not only addresses the specific concerns of the financial industry but also extends to any sector dealing with data protection issues in collaboration with machine learning models.

Ultimately, secure collaborative AI allows organizations to collaborate while mitigating risks and ensuring compliance with stringent privacy regulations, overcoming the roadblocks encountered in scenarios like the one between this financial institution and the tech firm.

Data Security Solutions with Duality Tech

Privacy concerns are at an all-time high which highlights the need for new data protection strategies and technologies that safeguard sensitive information in use. This is where Duality’s secure data collaboration solutions provide a secure framework that allows organizations and governments to collaborate and use data while maintaining data protection standards. 

Founded by globally recognized cryptographers and data scientists, we empower organizations to securely collaborate on sensitive data. Our credibility is not mere opinion; it’s bolstered by our partnerships with industry leaders like AWS, DARPA, Intel, Google, Oracle, IBM, and the World Economic Forum (WEF), reflecting our commitment to safeguarding data and maximizing its potential. Our expertise in operationalizing privacy-enhancing technologies (PETs) is recognized in our secure collaborative AI solution which allows model owners and users to securely collaborate, ensuring the privacy of data and the protection of the model’s intellectual property. 

By leveraging multiple privacy-enhancing technologies (PETs), like federated learning (FL), fully homomorphic encryption (FHE), and secure multiparty computations (MPC), we enable organizations and government agencies to personalize models to their specific needs, while ensuring sensitive real-world data remains encrypted and secure throughout the ML model training and inference process. This approach streamlines the integration and utilization of machine learning models, helping manage the daunting task of overcoming these obstacles and allowing organizations to derive actionable insights and anticipate future outcomes without compromising data privacy. 

Contact us today to better understand how you can benefit from Duality Tech today!

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