Audit Record Aggregation in Dispersed Storage Networks
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Solution Overview
Problem
Existing data storage systems in computer networks face challenges in efficiently dispersing and encoding data across multiple storage units while maintaining data integrity and security, especially in the presence of storage unit failures, without the need for redundant copies.
Innovation Solution
A dispersed storage network (DSN) utilizing Cauchy Reed-Solomon encoding and decoding processes to distribute data across multiple geographically dispersed storage units, ensuring data integrity and security through error correction, with a managing unit for vault creation and integrity processing to handle failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is distributed across multiple storage units in a dispersed storage network, then data availability and fault tolerance are improved, but data integrity and security may deteriorate due to increased exposure to storage unit failures and potential security vulnerabilities
Solution Approach 1:
The patent segments data into multiple encoded slices distributed across different storage units. Each slice is an encoded portion of the original data, and no single slice contains the complete information. This segmentation approach allows the system to tolerate failures of individual storage units while maintaining data security, as the distributed encoded slices cannot be compromised to reveal the original data without sufficient slices.
Solution Approach 2:
The patent applies error encoding (such as Cauchy Reed-Solomon encoding) to data before distribution to storage units. This preliminary encoding action creates redundant encoded slices that can detect and correct errors, ensuring data integrity before the data is dispersed across the network. The encoding is performed in advance to prevent potential data loss or corruption during storage and retrieval operations.
2Manufacturing precision
If error correction mechanisms are implemented in dispersed storage networks, then data integrity is improved, but system complexity increases due to additional encoding and decoding operations
Solution Approach 1:
The patent employs mathematical encoding parameters (such as Cauchy Reed-Solomon encoding with specific generator matrices) to transform data into encoded slices. By carefully selecting encoding parameters like the number of slices, threshold for reconstruction, and error correction capability, the system achieves robust data integrity while managing computational complexity. The parameter optimization balances the trade-off between error correction strength and processing overhead.
3Reliability
If audit records are aggregated for each audit object, then security monitoring and compliance are improved, but storage space and processing overhead increase
Solution Approach 1:
The patent aggregates multiple audit records into a single consolidated audit object. Instead of storing separate audit records for each access or operation, the system merges related audit information into unified audit objects that can be stored and processed more efficiently. This merging reduces the total quantity of stored audit data while maintaining comprehensive security monitoring capabilities, as the aggregated records preserve all necessary audit information in a compact format.
Data Source
AI summary
A storage network operates by: obtaining audit records, wherein each of the audit records indicates: a timestamp for a corresponding message, at least one event type code selected from a plurality of event type codes for a corresponding audit event of the corresponding message, and an identifier for a corresponding system entity associated with at least one event corresponding to the at least one event code; aggregating a number of audit records over a period of time; generating an audit file to include the number of audit records and integrity information; and facilitating storage of the audit file by utilizing a name of the audit file.