Privacy-Enhancing Technologies for Artificial Intelligence-Enabled Systems (2404.03509v1)

Abstract: AI models introduce privacy vulnerabilities to systems. These vulnerabilities may impact model owners or system users; they exist during model development, deployment, and inference phases, and threats can be internal or external to the system. In this paper, we investigate potential threats and propose the use of several privacy-enhancing technologies (PETs) to defend AI-enabled systems. We then provide a framework for PETs evaluation for a AI-enabled systems and discuss the impact PETs may have on system-level variables.

• The paper presents a comprehensive framework for assessing PETs in the context of AI, focusing on secure computation and data privacy.
• It evaluates key methodologies such as Fully Homomorphic Encryption, Federated Learning, and Trusted Execution Environments to address vulnerabilities during data use.
• The study highlights the threat landscape of insider and outsider risks, emphasizing the need for performance trade-offs and readiness in PET integration.

Privacy-Enhancing Technologies for Artificial Intelligence-Enabled Systems

Introduction to Privacy-Enhancing Technologies (PETs)

AI models carry inherent privacy vulnerabilities that span from the development phase to deployment and inference stages. To safeguard AI-enabled systems against both internal and external threats, the adoption of privacy-enhancing technologies (PETs) has become crucial. PETs aim to protect data during its use by allowing secure computation on sensitive information without revealing the actual data or compromising the system's functionality. This paper provides an extensive evaluation of PETs, presenting a framework for assessing their suitability in AI contexts and discussing their potential to reshape system-level security paradigms.

Data In Use: The Core Concern

The term "data in use" capture scenarios where data is being actively processed, whether by humans (e.g., data analysis) or machines (e.g., model training, inference). Unlike data at rest or in transit, data in use is vulnerable to exposure, necessitating innovative protection methods beyond traditional encryption and access control measures. This is particularly significant in AI systems, where data is not merely processed but plays a central role in model training and inference, introducing unique privacy challenges.

The Significance of PETs

PETs are designed to enable collaboration and data sharing with privacy as the foremost priority. These technologies offer several capabilities, including:

• Secure collaborative analytics
• Insight extraction from sensitive data without disclosure
• Trusted computation in untrusted environments
• Quantum-resistant encryption methods

Among the categories of PETs, Fully Homomorphic Encryption (FHE), Federated Learning (FL), and Trusted Execution Environments (TEEs) are positioned as transformative solutions for addressing AI-specific privacy concerns.

Understanding the Threat Landscape

PETs must contend with diverse threats that compromise the confidentiality and integrity of AI models and data. Insider threats (both careless and malicious) and outsider threats present distinct challenges, necessitating tailored PET solutions. For instance, careless insiders might inadvertently expose sensitive data to external AI services, while outsiders might attempt to reverse-engineer AI models to glean sensitive information.

PETs in Action: Architectural Solutions

• Trusted Execution Environments (TEEs) ensure isolated, secure computation spaces, safeguarding the execution from external visibility or alteration.
• Fully Homomorphic Encryption (FHE) enables operations on encrypted data, offering a robust shield for AI inference without compromising privacy.
• Federated Learning (FL) epitomizes collaborative model training with privacy, allowing multiple entities to contribute without exposing their data sets.

Each PET addresses specific phases of the AI model lifecycle, from development/training to deployment/inference, showcasing the multifaceted approach required to secure AI systems effectively.

Evaluating PETs for AI Systems

Selecting the appropriate PETs for an AI-enabled system entails rigorous evaluation of the use case applicability, system impact, and implementation readiness. The process involves understanding the PET's technical objectives, threat mitigation capabilities, and the trade-offs in performance or functionality. Moreover, readiness assessment, both in terms of technology maturity and organizational preparedness, is pivotal for successful PET integration.

Concluding Remarks

The integration of PETs into AI systems underscores a critical evolution in cybersecurity, extending protection to data in use. This advancement is not merely a technical necessity but a moral imperative to uphold the principle of "do no harm" in the era of AI. As AI technologies proliferate, PETs offer a pathway to mitigate privacy risks and foster trust in AI applications, aligning technological progress with ethical considerations.