Unlocking The Future Of AI With Lit Protocol

How Lit Protocol Revolutionizes Key Management and Privacy-Preserving AI Computations

By Drew Manley.

What is Privacy-Preserving AI Computation?

Privacy Preserving AI computations are effectively the ways in which Artificial Intelligence operates or engages with data while simultaneously preserving the confidential nature of the information. The techniques for doing so allow users to analyze data, gain insights, train further LLM models, and more, all without revealing preliminary data. Privacy preserving computations are made possible through methods such as encryption, anonymization, differential privacy, federated learning, and homomorphic encryption.

• Encryption: Transforming data into a coded format to prevent unauthorized access.
• Anonymization: Removing or altering personal identifiers from data sets.
• Differential Privacy: Adding noise to data to obscure individual entries.
• Secure Multi-Party Computation: Allowing parties to jointly compute a function over their inputs while keeping those inputs private.
• Federated Learning: Training AI models across multiple decentralized devices without sharing raw data.
• Homomorphic Encryption: Enabling computations on encrypted data without decrypting it.

These approaches allow data to be analyzed or processed in a way that prevents unauthorized access or disclosure of sensitive information while still enabling useful computations. In essence, privacy-preserving AI computations strike a balance between the utility of AI technologies and the protection of individuals’ privacy rights, ensuring that valuable insights can be gained from data without compromising confidentiality or violating privacy regulations.

Why is Data Privacy Important?

Data privacy is paramount in various areas due to its significant impact on personal and organizational security. Here’s why it’s crucial:

• LLM Models: Large language models (LLMs) require vast amounts of private data for training, making privacy essential to protect sensitive information.
• Personal Autonomy: Ensuring privacy upholds individuals’ rights to control their personal information.
• Uphold Trust from Clients: Maintaining data privacy builds and sustains trust with clients and customers.
• Security: Protecting personal data is crucial to prevent identity theft and other forms of cybercrime.

The Problem with Web2 Key Management Solutions

When working in areas where extreme privacy is crucial, like privacy-preserving AI computations, typical web2 key management solutions fall short. What once was viewed as untraceable and secure is now beginning to become decrepit, leading to security breaches and data leaks. Solutions such as those listed are no longer optimal:

Singular Key Management: When a single key is responsible for securing data, any breach means total exposure. If this key is leaked, the damage is immediate and comprehensive. The simplicity of this system is its downfall — there is no safety net.

Keys Distributed Among Multiple People: Distributing keys among multiple individuals seems like a safer approach, but it introduces new vulnerabilities. Humans can be manipulated, deceived, or phished, leading to potential security failures. This method might spread risk but doesn’t eliminate it.

Fireblocks: Fireblocks offers a more secure way to store keys using threshold cryptography, ensuring that no single key or person is entirely responsible for security. However, this solution is often expensive, non-credibly neutral (corporations discriminate between choosing clientele), and exposed to regulation risk. Consequently, resulting in inequitable access for organizations and people alike.

Threshold Cryptography with Lit Protocol

The Lit Protocol leverages the power of threshold cryptography to redefine key management and privacy-preserving AI computations. Here’s how it works and why it stands out:

Lit Protocol offers a distributed key management service, enabling users to generate a key known as a Programmable Key Pair (PKP). This key is not stored in a single location but across multiple nodes within the Lit network. When access conditions are met, the content is decrypted. It is important to note that the key never fully exists as a whole. When access control conditions are met, each node provides the end user with a key share. Once the user collects more than two-thirds of these shares, they can decrypt the content, but the key itself is never fully recombined. This process ensures that the content can only be signed when specific, pre-defined conditions are satisfied, thereby adding an additional layer of security.

Connecting Threshold Cryptography and Data Privacy for AI with Lit Protocol

• Enhanced Data Privacy: Threshold cryptography, as implemented by Lit Protocol, ensures that sensitive data used in AI computations is never fully exposed. By splitting the data encryption keys into multiple key-shares across several nodes, Lit Protocol ensures that even in the event of a key-share being lost, stolen, misplaced, etc., the protected data is still secure. This makes it nearly impossible for unauthorized parties to reconstruct the private key, therefore maintaining privacy of the underlying data being fed to the AI model.
• Federated Learning: With Lit Protocol’s key management, federated learning can be conducted securely. In federated learning, AI models are trained across decentralized devices holding local data samples, without sharing the actual data. Lit Protocol’s key management software ensures that the keys used in this distributed training process are managed securely, further protecting data privacy.

The adoption of Lit Protocol means enhanced security, better privacy, and more robust key management for users and organizations. It mitigates the risks associated with traditional key management methods and provides a scalable, cost-effective solution. Moreover, it enables privacy-preserving AI applications, ensuring that sensitive data remains protected while still deriving valuable insights.