At the heart of Terminal 3’s products lies an innovative zero knowledge database, designed to address key concerns around data security and provability. Leveraging advanced cryptographic techniques, it ensures user data is stored securely while allowing valid queries to be made by clients without compromising privacy. This database represents the cutting edge of zero-knowledge cryptography in database design.

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Problem

The need to store user data safely is paramount, but traditional databases often struggle with ensuring both data privacy and the ability to efficiently make valid queries. As consumers and regulators demand more protection guarantees, it makes sense to explore new approaches to database design and outsourcing.

To go down this path, clients would need verifiable results from their queries, while maintaining confidence that their data is secure and untampered.

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Solution

Our zkDB offers a powerful solution. By utilizing zero-knowledge proofs, it allows guarantees that the database includes all user data and hasn’t been tampered with; subsequent queries can be conducted in a provable and efficient manner.

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Key Properties

• Authenticated Data Structures (ADS): The database operates using an authenticated data structure with a single, updateable proof.
• Efficient Proofs: It is quick to check the validity of the entire database through that single proof, which can be updated as changes are made.
• Provable Queries: Clients can make queries and receive results that are provably correct and confirmed to come from the ADS.
• Data Protection: The zkDB prioritises user data protection, ensuring that private data is never exposed during query or update processes.

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Architecture - ADS

The architecture of the zkDB is built around a Merkle tree, and each update is tied to a proof that maintains the integrity of the entire database. Here’s an overview of the process:

1. Initialisation: The system begins with an empty Merkle tree that serves as the backbone of the zkDB.
2. User Update: When a user updates their data, it is added to the Merkle tree.
3. Proof Generation: After each update, the system generates a proof of the valid update.
4. Repetition: The process is repeated with each new update, maintaining a continuous chain of actions with proofs of the validity of each.

This structure allows the zkDB to maintain a secure and provable history of updates while protecting user data and enabling efficient queries. In the image below, you can see an overview of the Authenticated Data Structure (ADS) that underpins the zkDB, and how successive updates can be made.

Instead of needing to verify each update, we can batch updates and verify many of them at once, allowing very fast verification of the entire database. This is a key feature of the zkDB, enabling efficient and secure data storage and retrieval.

For example, we can batch all updates from a single day on the following day, and then send that batch proof to a smart contract for verification. Since this smart contract will have verified each batch proof, it can be trusted to verify the entire databse. Below you can see an image of how this batching process works, where all individual proofs from a single day can be batched together into a single proof.

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Architecture - SQL

In order to allow efficient and provable queries, we have developed a SQL-like query language that is compatible with zero knowledge proofs, and that is compatible with our zkDB.

We use bilinear accumulators to aggregate all addresses of users that have a particular property. We then put this accumulator in its own leaf of a Merkle tree, allowing us to grab it later when we need to know that all contained addresses will have that property. For example, below is an sketch of how we store data on the location of some users.

Note that the leaves only contain the commitment to the accumulator $A(\tau)$ and a key such as Location:London; the hash is of the concatenation of these two items as that is what we want to verifiably access later.