Decentralized Database Hash Pointers for Secure Low-Latency Access
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Solution Overview
Problem
Existing datastores are vulnerable to attacks by malicious insiders who can modify or exfiltrate confidential data, and traditional security measures are inadequate, especially when attackers mask their activities by deleting access logs.
Innovation Solution
Implement a system where data is stored with pointers to its actual location in a secure distributed storage, using cryptographic hash pointers and blockchain technology to ensure data integrity and security, while maintaining low latency for large data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is stored in traditional datastores with direct access, then ease of operation is improved, but security against insider threats deteriorates
Solution Approach 1:
The patent introduces cryptographic hash pointers as an intermediary layer between the datastore and users. Instead of directly accessing data, users obtain hash pointers that serve as secure references. This intermediary mechanism maintains ease of access while preventing unauthorized modification by insiders, as any alteration would change the hash value and break the reference chain.
Solution Approach 2:
The patent creates cryptographic copies (hashes) of data that serve as immutable references. These hash copies are stored in the datastore while the actual data resides in secure distributed storage. The copying process enables easy access through hash references while ensuring security, since the hash copies cannot be altered without detecting the change in the distributed ledger.
2Reliability
If data is masked and stored in secure distributed storage, then security is improved, but access speed deteriorates
Solution Approach 1:
The patent segments data access into two fast operations: retrieving hash pointers from the local datastore (which occurs instantly) and accessing actual data from secure distributed storage (which occurs only when needed). This segmentation eliminates the need to transfer or process large amounts of data through the middle layer, maintaining low latency while preserving security.
Solution Approach 2:
The patent performs preliminary actions by pre-storing cryptographic hash pointers in the local datastore before actual data access is needed. This preliminary caching of reference information enables instant access initiation, while the actual data retrieval from secure distributed storage happens only when specifically required, minimizing overall access time while maintaining security.
3Reliability
If cryptographic hash pointers are used to replace direct data storage, then data integrity is improved, but device complexity increases
Solution Approach 1:
The patent makes the cryptographic hash pointer system universal by designing it to work with existing database operations and data structures. The hash pointers integrate seamlessly with standard SQL queries, joins, and database protocols, allowing the system to maintain data integrity through cryptography while preserving the simplicity of conventional database operations. This multi-functionality reduces the perceived complexity for users.
Solution Approach 2:
The system performs self-service by automatically generating, storing, and verifying cryptographic hash pointers without requiring manual intervention. The database management system handles the complexity of cryptographic operations, hash generation, and verification internally, while presenting a simplified interface to users. This self-service approach masks the underlying complexity while ensuring data integrity.
Data Source
AI summary
Techniques for managing data stored within a database, such as a decentralized database are provided. Some techniques involve managing some data within a lower-trust database and some other data within a higher-trust database. A higher-trust database may be a decentralize database including a blockchain. A lower-trust database may store references to data within the blockchain, and optionally other data in association with those references. Disclosed techniques include WHERE clause query handling in databases with reference values, replacement of distinct data in a relational database with a distinct reference to that data, number line storing for secure indexing, APIs for databases, and consensus operations for private blockchain networks.