In the age of data-driven decision making, businesses are utilizing machine learning (ML) to gain valuable insights, improve operational efficiencies, and establish a competitive advantage.

While recent advancements in generative artificial intelligence (AI) have highlighted the potential of AI/ML, they have also emphasized the importance of privacy and security. Organizations seeking to capitalize on the advantages of AI without increasing their risk level are advised to consider recommendations from groups like IAPP, Brookings, and Gartner’s AI TRiSM framework.

One crucial aspect to address is the security of ML models. Privacy-preserving machine learning has emerged as a solution to ensure users can fully benefit from ML applications in this critical field while preserving privacy.

Utilizing machine learning to generate insights

Machine learning models are algorithms that process data to provide meaningful insights and inform crucial business decisions. What sets ML apart is its ability to continuously learn and improve. As models are trained with new and diverse datasets, they become more intelligent over time, offering precise and valuable insights that were previously unattainable. These models can then be used to extract insights from data, a process known as model evaluation or inference.

To achieve optimal outcomes, models need to learn from and leverage various rich data sources. However, when these sources contain sensitive or proprietary information, using them for ML model training or evaluation raises significant privacy and security concerns. Any vulnerability in the model becomes a liability for the organization utilizing it, counteracting the potential benefits of actionable insights and increasing the organization’s risk profile.

This challenge remains a major obstacle hindering widespread adoption of ML. Businesses must navigate the trade-off between the benefits of ML and the need to protect their interests while complying with evolving privacy and regulatory requirements.

Vulnerabilities in ML models

Vulnerabilities in ML models typically fall into two main categories: model inversion and model spoofing.

Model inversion attacks involve targeting the model itself to reverse engineer the data on which it was trained. This data often includes sensitive information, such as personally identifiable information (PII) and intellectual property (IP), which can cause significant harm if exposed.

On the other hand, model spoofing represents a form of adversarial machine learning where attackers manipulate input data to deceive the model into making incorrect decisions that align with their intentions. By carefully observing and “learning” the model’s behavior, attackers alter the input data in imperceptible ways to trick the model, serving their own objectives. Both of these attacks exploit vulnerabilities related to model weights, a crucial component of ML models. The need to prioritize model weight protection was emphasized during a recent discussion on AI risk convened by the White House.

Using privacy enhancing technologies

Privacy-preserving machine learning utilizes advancements in privacy enhancing technologies (PETs) to tackle these vulnerabilities head-on. PETs are a group of technologies that enhance and protect the privacy and security of data throughout its processing lifecycle, enabling secure and private data usage. With these powerful technologies, businesses can encrypt sensitive ML models, run or train them, and extract valuable insights without the risk of exposure. Organizations can securely leverage diverse data sources, even across different security domains and organizational boundaries, even when competitive interests are at play.

Two prominent pillars of PETs that enable secure and private ML are homomorphic encryption and secure multiparty computation (SMPC).

Homomorphic encryption allows businesses to perform encrypted computations on data, preserving the privacy of search or analytic content. By homomorphically encrypting ML models, organizations can run or evaluate them using sensitive data sources without exposing the underlying model data, enabling the use of models trained on sensitive data outside trusted environments.

With SMPC, organizations can train models in an encrypted capacity, protecting the model development process, training data, and the interests of involved parties. Models can be collaboratively trained on sensitive data without the risk of exposure. This approach ensures privacy, security, and confidentiality while harnessing the collective power of diverse datasets to enhance the accuracy and effectiveness of ML models.

Conclusion

The growing reliance on machine learning to enhance business operations is a lasting trend, accompanied by significant ML model risks. Once an organization recognizes the core value that AI/ML can bring, addressing security, risk, and governance becomes the next crucial step towards adoption.

Advancements in PETs provide a promising path forward. Privacy-preserving machine learning allows organizations to unlock the full potential of ML while upholding privacy, complying with regulations, and safeguarding sensitive data. By embracing this security-forward approach, organizations can confidently navigate the data-driven landscape, harness valuable insights, and maintain the trust of customers and stakeholders.

Fabio

Full Stack Developer

About the Author

I’m passionate about web development and design in all its forms, helping small businesses build and improve their online presence. I spend a lot of time learning new techniques and actively helping other people learn web development through a variety of help groups and writing tutorials for my blog about advancements in web design and development.

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