google/trillian

Features

• Verifiable Storage - Stores data in a cryptographically verifiable structure using Merkle trees to ensure transparency and integrity.
• Merkle-Tree Storage Engines - Organizes data into a cryptographic Merkle tree structure to enable efficient inclusion and consistency proofs.
• Technical Transparency Log Infrastructure - Builds systems that allow public auditors to verify the integrity and consistency of a shared set of records.
• Signed Root Distribution - Provides the latest signed root of the Merkle tree to establish the current state and size of the log.
• Cryptographic Commit Logs - Creates append-only data stores using Merkle trees to ensure records cannot be altered or deleted without detection.
• Append-Only Consistency Proofs - Creates cryptographic proofs demonstrating that a newer log version is an append-only extension of an older one.
• Distributed Consensus Stores - Provides a high-availability data store using leader election and consensus to coordinate verifiable logs.
• Log Tenant Isolation - Isolates different data streams by identifier by hosting multiple independent Merkle trees for different tenants.
• Proof-Carrying Data Stores - Runs high-availability clusters providing cryptographic proofs of inclusion and consistency for large datasets.
• Append-Only Log Feeds - Adds entries to an append-only Merkle tree using consecutive queuing or predefined ordering.
• Distributed Leader Election - Coordinates a single active master node among a distributed cluster to ensure high availability.
• Verifiable Tree Hosting - Hosts several independent Merkle trees within a single installation to support multi-tenancy.
• Cryptographic Inclusion and Consistency Proofs - Produces inclusion and consistency proofs allowing clients to verify data without downloading the entire dataset.
• Leaf Inclusion Proofs - Generates cryptographic proofs to verify that a specific entry exists within a given tree root.
• Proof Generation - Produces cryptographic Merkle proofs for specific leaf indices or hashes within the tree.
• Threshold-Signed State Management - Maintains a history of signed tree heads to establish the authoritative state and size of the log.
• Sparse Merkle Trie State Verification - Implements a key-value store using a sparse Merkle tree to provide verifiable mapping of keys to values.
• Data Canonicalization - Converts equivalent formulations of data into a single format to prevent duplication of identical entries.
• SQL-Backed Stores - Persists cryptographically hashed trees and maps across various relational database backends.
• Pluggable Database Backends - Integrates with various relational databases to persist verifiable data.
• Relational Storage Backends - Offloads the storage of tree nodes and leaf data to relational databases for durability and scalability.
• Tamper-Proof Database Managements - Records system events or financial transactions with mathematical proof of the sequence and content.
• Verifiable Map Generation - Creates cryptographically verifiable maps using a pipeline approach to scale efficiency for large datasets.
• Verifiable Map Leaf Management - Writes and verifies individual or batch leaves in a sparse Merkle tree map.
• Admission Request Validation - Validates submissions and canonicalizes data using application-specific logic to manage external interfaces.
• Gossip Protocols - Uses a gossip protocol to synchronize observer views and detect split-view attacks.
• Certificate Transparency - Maintains a verifiable log of digital certificates to prevent fraudulent issuance and improve PKI security.
• Signing Key Rotation - Updates private signing keys via an administrative interface to maintain the trust chain without altering storage.
• Inclusion Commitments - Provides a signed commitment to incorporate a submitted entry within a defined time window.
• Data Submission Validators - Validates that incoming data is signed by an authorized entity and follows a specific required structure.
• Signing Key Lifecycle Management - Handles cryptographic keys and signing operations using hardware modules or standard file formats.
• Split-View Attack Detection - Shares observer views via a gossip protocol to ensure all users see the same data.
• Verifiable Map Integrity Checks - Checks if a specific key-value pair is correctly committed within a set of map tiles.
• Application Logic Extensions - Pairs a data store with a personality layer to handle admission criteria and data canonicalization.
• Iterable and Mapping Content Validators - Applies structural and authentication checks to entries to ensure they meet format and trust requirements.
• Admission Layers - Decouples core storage mechanisms from application-specific admission criteria and data canonicalization rules.

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