How To Protect Privacy While Using Machine Learning?

Businesses are using ML to gain useful insights in the era of data-driven decision-making

Businesses are utilizing machine learning (ML) to generate operational savings, strengthen their competitive edge, and reveal priceless insights in the age of data-driven decision-making. The power of AI/ML has recently attracted unprecedented attention, yet recent advances in generative AI have also brought to light the fundamental necessity for privacy and security. Key factors have been articulated for firms aiming to accomplish the commercial benefits made possible by AI without raising their risk profile by groups like IAPP, Brookings, and Gartner's latest AI TRiSM framework.

The security of ML models is at the forefront of these requirements. In order to ensure that consumers may fully benefit from ML applications in this increasingly vital industry, privacy-preserving machine learning has evolved as a solution that directly addresses this critical topic.

Machine Learning is Being Used to Produce Insights:

Algorithms used in machine learning models process data to produce insightful findings and support important business choices. The capability of ML to continuously learn and advance is what makes it unique. A model gets wiser over time as it is trained on new and diverse datasets, producing increasingly precise and worthwhile insights that weren't previously available. Model evaluation or inference is the process of using these models to draw conclusions from data.

Models must be trained on and/or used with a variety of rich data sources to produce the best results. Using these data sources for machine learning model training, evaluation, or inference creates serious privacy and security risks when they contain sensitive or private information. This capability, which promised to give business-enhancing, actionable insights, is now raising the organization's risk profile. Any vulnerability of the model itself becomes a liability for the entity utilizing it.

Security Flaws in ML Models:

Model inversion and model spoofing are two common attack vectors that are brought on by ML model flaws. Targeting the model itself allows for a reverse engineering assault on the data that was used to train it, which is presumably sensitive and valuable to the attacker. This could include intellectual property (IP), personally identifiable information (PII), and other sensitive or regulated data that, if leaked, could have a disastrous effect on the firm.

Model spoofing, on the other hand, is a type of adversarial machine learning in which an attacker tries to trick the model into making false judgments that are in line with the attacker's goals by altering the input data. In order to mislead the model into making decisions that are advantageous to their goals, this approach entails carefully watching or "learning" the behavior of the model and then changing the input data.

Using Technology That Enhances Privacy:

Modern privacy-enhancing technologies (PETs) are used by privacy-preserving machine learning to address these weaknesses. A class of technologies known as PETs uniquely enables secure and private data usage by preserving and enhancing data security and privacy throughout the processing lifecycle. With the use of these potent technologies, businesses may run and/or train sensitive ML models, derive useful insights from them, and do so without running the risk of exposure. Even when there are competing interests, businesses can safely use various data sources that span organizational borders and security domains.

Homomorphic encryption and secure multiparty computation are two significant foundations of the PETs family that enable safe and private ML (SMPC). Through the use of homomorphic encryption, organizations can execute encrypted computations on data while maintaining the confidentiality of the search or analysis's content. Organizations can execute or evaluate machine learning (ML) models on sensitive data sources without disclosing the underlying model data by homomorphically encrypting the models. This enables models trained on sensitive data to be used outside of their trusted environment.