Memory Tracing in Asset Management for Privacy Control
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Solution Overview
Problem
Existing asset management systems fail to effectively track and manage memory-based components in large networks, such as cloud networks and data centers, which can lead to unauthorized access and compromise of sensitive data due to discrepancies in memory specifications and lack of proper data privacy management.
Innovation Solution
An asset management system that compares memory specifications from new devices with a volatility database to generate a memory-asset data structure, assigns data privacy levels based on sensitivity, and performs operations like erasure or disposal of components to prevent data leaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory components are tracked and managed in asset management systems, then data security and privacy are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments memory management by creating separate data structures for different memory components (volatile memory, non-volatile memory, cache, registers) and their associated specifications. Each memory component is tracked independently with its own set of attributes, allowing granular control and management while maintaining overall system security.
Solution Approach 2:
The patent introduces an intermediary asset management system that acts as a mediator between hardware components and users. This system includes a volatility database and comparison mechanisms that automatically manage memory specifications and security levels, reducing the burden on users while maintaining high security standards.
2Measurement precision
If memory specifications are compared with volatility database, then accuracy of data classification is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-populating a volatility database with known memory specifications and security classifications before actual memory tracing begins. This allows the system to quickly compare new memory components against established benchmarks, reducing processing time while maintaining high classification accuracy.
Solution Approach 2:
The patent utilizes parameter changes by comparing multiple specifications (size, type, volatility, location) of memory components against the database. By changing and comparing these parameters systematically, the system achieves precise classification while optimizing the comparison process to reduce computational overhead.
3Reliability
If data privacy levels are assigned based on sensitivity, then data protection is improved, but management overhead and operational complexity increase
Solution Approach 1:
The patent implements self-service by enabling the asset management system to automatically assign data privacy levels based on pre-defined policies and memory specifications. The system autonomously categorizes memory components and applies appropriate protection levels without requiring manual intervention, reducing management overhead while maintaining strong data protection.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors memory usage and security events, automatically adjusting privacy levels and management policies based on observed patterns and threats. This feedback loop reduces operational complexity by eliminating the need for constant manual policy adjustments while maintaining high data protection standards.
Data Source
AI summary
The present embodiments relate to implementing change data on no-master NoSQL data stores. An optimized node can be identified from a plurality of NoSQL data storage nodes and a specialized node can be connected (e.g., collocated) to the optimized node. The specialized node can maintain change data capture (CDC) data provided by client nodes in a hash map that can be used as a point of truth for coordinating CDC data across the plurality of NoSQL data storage nodes. The plurality of NoSQL data storage nodes can identify and coordinate all read/write data obtained from multiple client devices in a geographically separated large-scale (e.g., planet scale) system to identify change data in a distributed data store. The specialized data can provide read data to devices in the large-scale system to reconcile inconsistencies in change data across nodes in the large-scale system.