Deep dive into zkML with Aleo

Machine learning:

Machine learning has become an integral part of our technological landscape, powering various applications and driving innovation. However, concerns about privacy and the security of sensitive data have raised important questions regarding the confidentiality of machine learning models and the protection of user information. In response to these challenges, the concept of zero knowledge (ZK) has emerged as a powerful paradigm to enable privacy-preserving machine learning. In this blog, we will explore the intersection of zero knowledge and machine learning, highlighting the potential of zero knowledge machine learning (ZKML) to revolutionize AI while safeguarding privacy.

Understanding Zero Knowledge:

Zero knowledge is a cryptographic concept that allows one party (the prover) to convince another party (the verifier) of the truth of a statement without revealing any additional information beyond the statement’s validity. This approach provides a means to achieve privacy and confidentiality while still ensuring the integrity of the information being shared.

Applying Zero Knowledge in Machine Learning:

Zero knowledge techniques can be applied to various aspects of machine learning, providing privacy guarantees and enabling secure data analysis. Here are some key areas where zero knowledge plays a crucial role:

• Authenticity Verificatio:

You need to be sure that the machine learning model said to be used by an entity is indeed the one utilized. An instance would be when a model provider offers multiple versions, with varying price points and accuracy, and you wish to confirm that the higher-performance model you’ve paid for is the one you’re getting. This could be done by implementing a zero-knowledge commitment scheme such as functional commitments, as proposed by Dan Boneh, Wilson Nguyen, and Alex Ozdemir. With this, the model owner can assure that the declared model was executed. Some applications built on Risc Zero, a general-purpose STARK-based VM, and other studies also offer similar verification.
However, it’s also important to verify that the implemented model is accurate, requiring third-party audits. Although functional commitments verify that a stated model was used, they don’t provide insights about the committed model’s accuracy. Future advancements could potentially offer these guarantees.

• Consistency Assurance:

You require confirmation that a machine learning algorithm is consistently applied across different user data. This is crucial in sectors like credit scoring and loan applications where bias needs to be avoided. This could be achieved using functional commitments or by making the model and its parameters public, enabling users to confirm their data was processed correctly. This approach could be particularly beneficial in healthcare, where data confidentiality is mandatory.

• Verification Attestations:

You wish to incorporate attestations from verified external parties into a model or smart contract running on-chain. This could be achieved by using a zero-knowledge proof to validate a digital signature. Recent advancements in this field have been explored in an episode of the Zero Knowledge podcast hosted by Anna Rose and Tarun Chitra.
Moreover, there are ways to verify that images captured by attested sensors have undergone permissible modifications like cropping or resizing. Any digitally signed information could be a candidate for this verification.

• Decentralized Processing:

You want to perform machine learning operations in a decentralized manner, with public data submission. This could be facilitated by deploying an existing model on-chain or creating a new network, and using zero-knowledge proofs to compact the model. Jason Morton’s EZKL library offers a method to convert ONXX and JSON files into ZK-SNARK circuits, enabling verification of large parameter models. Other groups, like Modulus Labs, are testing different proof systems for on-chain processing, and Gensyn is developing a decentralized compute system for training models.

• Identity Proofing:

You aim to confirm someone’s unique personhood without infringing on their privacy. This could be executed by designing a verification process, like biometric scanning or encrypted government ID submission, and using zero-knowledge proofs to confirm the verification without revealing personal information.
An example of this is Worldcoin’s proof-of-personhood protocol, where unique iris codes ensure user uniqueness without revealing any identifiable information. This example demonstrates how the privacy-preserving properties of zero-knowledge proofs can be used to counter potentially malicious artificial intelligence applications.

Challenges and Advances in Zero Knowledge Machine Learning:

While zero knowledge techniques offer promising solutions, there are challenges to overcome. One major challenge is the computational overhead associated with performing cryptographic operations, which can impact the scalability and efficiency of ZKML systems. However, ongoing research and advancements in cryptographic protocols and hardware acceleration are addressing these challenges.

Recent developments in ZKML have shown great potential. For example, researchers have explored the use of zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge) to create compact and verifiable proofs for machine learning computations. These proofs allow verifiers to validate the correctness of a computation without needing to rerun the entire process, reducing computational costs.

Additionally, federated learning, a distributed learning approach, combined with zero knowledge techniques, offers a promising direction for privacy-preserving machine learning. Federated learning allows training on user devices, with the models aggregated while preserving privacy through encryption and differential privacy techniques. Zero knowledge protocols can further enhance privacy by ensuring that no sensitive information is leaked during the aggregation process.

Now let’s talk a little bit about Aleo:

Aleo, a privacy-focused blockchain platform, is making significant contributions to the field of zero knowledge machine learning. Aleo leverages zero knowledge proofs and zk-SNARKs to enable private and verifiable smart contracts, including those involving machine learning models. With Aleo, developers can build privacy-preserving applications that leverage machine learning capabilities while ensuring data confidentiality.

Aleo’s platform allows for secure computation and private data transfer, ensuring that sensitive information remains encrypted throughout the machine learning process. This approach opens up new possibilities for decentralized applications that require privacy-sensitive machine learning tasks, such as healthcare data analysis or financial predictions.

By combining the power of zero knowledge proofs, zk-SNARKs, and decentralized technologies, Aleo is pioneering a path towards privacy-focused machine learning. Their efforts contribute to the broader landscape of zero knowledge machine learning and highlight the potential for privacy-enhancing technologies to reshape the future of AI.

Conclusion

In conclusion, zero knowledge machine learning, bolstered by advancements from projects like Aleo, offers a transformative approach to address privacy concerns in the field of AI. Through the application of zero knowledge proofs and cryptographic techniques, we can unlock the potential of machine learning while preserving user privacy and data confidentiality. Aleo’s contributions demonstrate the feasibility of privacy-preserving smart contracts involving machine learning models, paving the way for innovative applications in various domains. As research and development in this field continue to progress, we can expect to see further innovations and applications that empower individuals and organizations with privacy-preserving AI systems.