The Curious Case of Confidential Computing

What is Confidential Computing, and why is it important for the future of Cloud and AI? How can we secure data exchange and enable collaboration in the development of AI algorithms? New technologies — like Ultraviolet’s Prism AI platform — bring collaboration based on secure and attestable hardware and software environments, opening up a new set of possibilities.

AI Needs Data

Artificial Intelligence (AI) thrives on data. The more data an AI system has access to, the better it can learn, adapt, and perform its tasks. Data serves as the foundational element for training AI models, enabling them to recognize patterns, make predictions, and automate complex processes. High-quality, diverse datasets are crucial as they enhance the robustness and accuracy of AI models, making them more reliable and effective in real-world applications.

Why AI Needs Data

1. Training Models: AI algorithms learn from data through a process called training. During training, models analyze large datasets to identify patterns and relationships within the data. This learning process is essential for developing models capable of performing specific tasks, such as image recognition, natural language processing, and predictive analytics.
2. Improving Accuracy: The accuracy of AI predictions and decisions heavily depends on the quality and quantity of the data it has been trained on. Larger datasets provide a broader range of examples for the AI to learn from, reducing the chances of overfitting and improving generalization to new, unseen data.
3. Enhancing Adaptability: Diverse datasets expose AI models to various scenarios and edge cases, making them more adaptable and resilient to changes. This diversity helps models perform well even when faced with data variations or anomalies in real-world situations.

Data Exchange and Collaborative AI

In the realm of Artificial Intelligence (AI), data exchange and collaboration are pivotal for driving innovation and achieving high levels of accuracy in AI applications. Data exchange refers to the process of sharing data between different entities, such as organizations, institutions, or even countries. Collaborative AI, on the other hand, involves multiple parties working together to develop, train, and refine AI models by pooling their data resources and expertise.

These concepts are becoming increasingly important as the complexity and scale of AI challenges grow. No single organization typically possesses all the necessary data or domain knowledge to create the most effective AI models. Therefore, fostering collaboration and data exchange is crucial for advancing AI capabilities. Here’s why:

1. Data Diversity: Individual organizations typically have access to limited and specific types of data. By collaborating with other organizations, AI developers can pool diverse datasets, covering a broader spectrum of scenarios and reducing biases. This diversity is essential for creating well-rounded AI models.
2. Volume of Data: Many AI applications, particularly those involving deep learning, require vast amounts of data. Collaborating with other organizations can help accumulate the necessary data volume more quickly and efficiently than any single organization could achieve alone.
3. Domain Expertise: Different organizations possess unique domain knowledge and specialized data. Sharing data across organizations allows AI models to benefit from this expertise, leading to more accurate and contextually relevant outcomes.
4. Regulatory Compliance: In many industries, data-sharing agreements can help meet regulatory requirements by ensuring that data used for AI training adheres to legal standards. Collaborative data sharing can also foster transparency and trust among stakeholders.

Privacy Enhancing Technologies (PETs)

Privacy Enhancing Technologies (PETs) are a suite of methods designed to protect the privacy and security of data during its processing, storage, and transfer. Among the most notable PETs are Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). These cryptographic techniques hold significant promise for enabling secure data operations but face practical challenges when applied to intensive computations, such as those required for AI.

Fully Homomorphic Encryption (FHE)

Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without needing to decrypt it first. This means that sensitive data can remain encrypted throughout processing, providing a high level of security and privacy.

Advantages of FHE:

• Data Privacy: Data remains encrypted at all times, ensuring that sensitive information is never exposed, even during computation.
• Security: Strong cryptographic foundations make it difficult for unauthorized parties to access or tamper with the data.

Challenges of FHE:

• Computational Overhead: FHE is computationally intensive. The encryption and decryption processes, along with performing operations on encrypted data, require significantly more computational resources compared to processing plaintext data.
• Performance: The current state of FHE technology results in considerable slowdowns, making it impractical for real-time or large-scale AI applications where performance is critical.

Secure Multi-Party Computation (SMPC)

Secure Multi-Party Computation (SMPC) is a cryptographic protocol that allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. Each party only knows their own input and the final result, but not the inputs of the other parties.

Advantages of SMPC:

• Collaboration: Enables secure collaborative computations between multiple parties without exposing their individual data.
• Privacy: Maintains the confidentiality of each party’s input, providing a secure way to jointly compute results.

Challenges of SMPC:

• Complexity: The protocols involved in SMPC are complex and require significant computational resources to maintain security guarantees.
• Inefficiency: Like FHE, SMPC can be inefficient for large-scale computations, such as those required in AI. The need for extensive communication and processing can lead to performance bottlenecks.

Practicality in AI Applications

While FHE and SMPC offer robust privacy protections, their practicality in AI applications is currently limited due to inefficiencies and high computational demands. AI models often require processing large datasets and performing complex calculations quickly. The overhead associated with purely cryptographic solutions like FHE and SMPC makes them impractical for these intensive tasks.

For example, training a deep learning model involves iterative calculations on massive datasets, which already demands substantial computational power. Introducing cryptographic methods into this process would exponentially increase the required resources, leading to impractically long computation times and increased costs.

Enter Confidential Computing

Confidential Computing is emerging as a practical and powerful solution to the limitations of purely cryptographic privacy-enhancing technologies (PETs) like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). At the heart of Confidential Computing are Trusted Execution Environments (TEEs), which provide a secure enclave within a processor, ensuring that data remains protected during processing.

Trusted Execution Environments (TEEs)

Trusted Execution Environments (TEEs) are secure areas within a main processor that isolate code and data from the rest of the system. These environments provide a high level of security by ensuring that data is protected while being processed, thus addressing the inefficiencies and impracticalities of purely cryptographic solutions.

Advantages of TEEs:

• Isolation: TEEs isolate sensitive computations from the main operating system, reducing the risk of data breaches and unauthorized access.
• Performance: Unlike FHE and SMPC, TEEs allow for secure processing without the significant computational overhead, making them suitable for intensive tasks like AI.
• Flexibility: TEEs can be used with various types of data and applications, providing a versatile solution for secure data processing.

TEEs can be used to protect AI, from the model training phase to inference

Confidential Computing in Practice

Confidential Computing extends the capabilities of TEEs by creating a secure and attestable environment for data processing across multiple systems and cloud environments. This approach ensures that data remains encrypted not only during storage and transit but also while it is being processed. Here’s why Confidential Computing is crucial for AI and other intensive computations:

1. Enhanced Security: Confidential Computing ensures that sensitive data is always protected, mitigating risks associated with data breaches and unauthorized access. This is particularly important for industries dealing with highly sensitive information, such as healthcare, finance, and government.
2. Performance Efficiency: By leveraging hardware-based security features, Confidential Computing enables efficient processing of large datasets and complex AI models without the substantial performance penalties associated with cryptographic methods like FHE and SMPC.
3. Scalability: Confidential Computing allows for scalable and secure data processing across cloud environments. This is essential for AI applications that require substantial computational resources and the ability to scale up quickly.
4. Regulatory Compliance: With the increasing focus on data privacy regulations, Confidential Computing provides a robust framework for ensuring compliance. It helps organizations meet stringent regulatory requirements by providing verifiable security guarantees for data processing.

Prism AI

Prism AI, developed by Ultraviolet, represents a groundbreaking approach to Collaborative AI and privacy-preserving data sharing. By harnessing the power of Trusted Execution Environments (TEEs) and Confidential Computing, Prism AI addresses the critical challenges of data privacy, security, and collaborative development in AI.

Secure enclaves ensure that data is never revealed to any of the computation participants, or anybody else. Only the result of the computation is returned to a Result Recipient that holds appropriate credentials.

How Prism AI Works

Prism AI leverages TEEs to create secure, isolated environments within processors, ensuring that data remains encrypted and protected throughout the computation process. This technology allows organizations to collaborate on AI projects without exposing their sensitive data to external risks. Here’s how Prism AI enhances Collaborative AI and privacy-preserving data sharing:

1. Secure Data Sharing: Prism AI enables organizations to share data securely across different entities, ensuring that sensitive information is never exposed during processing. TEEs isolate computations, allowing parties to work together on AI models while maintaining strict data confidentiality and integrity.
2. Attestable Security: One of the key features of Prism AI is its ability to provide attestable security. This means that the integrity and confidentiality of the data and computations can be independently verified, ensuring that all parties involved adhere to the highest security standards. This feature is crucial for building trust among collaborators and meeting regulatory compliance requirements.
3. Efficient Collaboration: By reducing the computational overhead typically associated with traditional cryptographic solutions like FHE and SMPC, Prism AI allows for more efficient data processing. This efficiency is essential for handling the large datasets and complex computations required in advanced AI applications, ensuring that collaborative projects are not only secure but also performant.
4. Scalable Infrastructure: Prism AI’s use of TEEs and Confidential Computing technologies provides a scalable infrastructure that supports the growth and expansion of AI projects. Whether it’s sharing datasets, training models, or running simulations, Prism AI ensures that the computational resources can scale seamlessly, maintaining security and performance across different stages of AI development.

Benefits of Prism AI for Collaborative AI

1. Enhanced Privacy: Prism AI’s use of TEEs ensures that data is encrypted and protected during processing, significantly enhancing privacy. This makes it possible to collaborate on AI models without compromising the confidentiality of the data, fostering a secure environment for innovation.
2. Reduced Risk of Data Breaches: By isolating computations within secure enclaves, Prism AI minimizes the risk of data breaches and unauthorized access. This robust security framework is essential for organizations handling sensitive or regulated data, providing peace of mind and safeguarding against potential threats.
3. Streamlined Regulatory Compliance: Prism AI’s architecture supports compliance with various data protection regulations, such as GDPR and HIPAA. The ability to verify and attest to the security and privacy of data processing ensures that organizations can meet legal requirements without complex and cumbersome compliance procedures.
4. Facilitated Innovation and Research: By enabling secure data sharing and collaboration, Prism AI opens new avenues for research and development in AI. Researchers and organizations can pool their data and expertise, accelerating innovation and driving advancements in AI technologies that benefit society as a whole.

Real-World Impact

Prism AI is already making waves in industries that require high levels of data security and collaboration. By providing a robust platform for privacy-preserving data sharing and secure AI development, Prism AI is helping organizations unlock the full potential of their data while adhering to the strictest security and privacy standards.

In conclusion, Prism AI, with its innovative use of TEEs and Confidential Computing, is transforming the landscape of Collaborative AI. It empowers organizations to work together securely and efficiently, paving the way for groundbreaking advancements in AI technology while ensuring that data privacy and security are never compromised.

To learn more about Ultraviolet Security, and how we’re building the world’s most sophisticated collaborative AI platform, visit our website and follow us on Twitter!