Businesses today are increasingly turning to machine learning (ML) to gain valuable insights, improve operational efficiency, and gain a competitive edge. However, as the importance of ML continues to grow, so does the need for privacy and security. The recent developments in generative artificial intelligence (AI) have brought attention to the need for organizations to prioritize privacy and security in their AI/ML initiatives.
Several groups, including IAPP, Brookings, and Gartner’s AI TRiSM framework, have emphasized the importance of considering privacy and security when implementing AI/ML solutions. One area of focus that these groups highlight is ML model security. Ensuring the security of ML models is crucial for organizations to fully leverage the potential of ML applications while minimizing their risk profile.
ML models are algorithms that process data to generate meaningful insights and inform critical business decisions. These models continuously learn and improve over time, making them increasingly accurate and valuable. To achieve the best outcomes, models need to be trained on diverse and rich data sources. However, when these data sources contain sensitive or proprietary information, using them for ML model training raises significant privacy and security concerns.
This issue poses a barrier to broader adoption of ML in business. Organizations must find a balance between reaping the benefits of ML and protecting their interests and complying with privacy and regulatory requirements.
Vulnerabilities in ML models typically fall under two categories: model inversion and model spoofing. Model inversion attacks involve reverse-engineering the model to access sensitive data over which it was trained. This type of attack can expose personally identifiable information, intellectual property, and other regulated information. Model spoofing, on the other hand, is an adversarial approach where attackers manipulate input data to deceive the model and cause it to make incorrect decisions.
To address these vulnerabilities, privacy-preserving machine learning leverages privacy enhancing technologies (PETs). PETs are a family of technologies that enhance the privacy and security of data throughout its processing lifecycle. Two important pillars of PETs are homomorphic encryption and secure multiparty computation (SMPC).
Homomorphic encryption allows organizations to perform encrypted computations on data, preserving the privacy of the content. By encrypting ML models, organizations can run or evaluate them on sensitive data sources without exposing the underlying model data. This enables the utilization of models trained on sensitive data outside of trusted environments.
SMPC enables collaborative training of models on sensitive data without the risk of exposure. It protects the model development process, training data, and the interests of involved parties. By leveraging SMPC, organizations can enhance the accuracy and effectiveness of machine learning models while ensuring privacy, security, and confidentiality.
The increasing reliance on machine learning in business is not a passing trend, and neither are the risks associated with ML models. Once the value that AI/ML can provide to an organization is established, the next step is to prioritize security, risk, and governance. Advancements in PETs offer a promising path forward, enabling organizations to securely harness the full potential of ML while upholding privacy and compliance with regulatory directives.
By adopting a security-forward approach to ML, organizations can confidently navigate the data-driven landscape, leveraging valuable insights while maintaining the trust of customers and stakeholders.