Database Access Observability for Column-Level Encryption Policies
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Solution Overview
Problem
Existing database systems struggle to manage column-level encryption effectively, leading to privacy concerns and database crashes due to the difficulty in creating precise Module Access Policies (MAPs) for multiple entities accessing complex tables.
Innovation Solution
Implement a data observability system that monitors table access within a predefined time window, logs access information, and generates automatic Module Access Policies (MAPs) based on this data, enabling column-level encryption and ensuring each entity has the necessary permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If column-level encryption is implemented to protect private data, then data privacy is improved, but access control complexity increases
Solution Approach 1:
The system performs preliminary monitoring of data access patterns during a monitoring period before generating access policies. It collects and analyzes access logs to understand which columns are accessed by which users, then uses this information to automatically generate appropriate access policies that balance security and usability.
Solution Approach 2:
The system automatically generates access policies based on monitored access patterns without requiring manual intervention. The policy generation is performed autonomously by analyzing the collected access logs and applying predefined rules to create appropriate column-level access control policies.
2Measurement precision
If manual creation of Module Access Policies is used, then policy precision is improved, but time consumption increases
Solution Approach 1:
The system automatically generates access policies by analyzing monitored access patterns. It processes access logs and autonomously creates policies without requiring manual intervention, thereby reducing time consumption while maintaining precision through data-driven decision making.
Solution Approach 2:
The system uses feedback from actual data access patterns to generate access policies. By continuously monitoring and analyzing access logs, it adjusts and refines policies to better match actual usage patterns, improving both precision and efficiency.
3Measurement precision
If access monitoring is performed to generate policies, then access control accuracy is improved, but system overhead increases
Solution Approach 1:
The system monitors access patterns for a predefined period and focuses monitoring on specific columns that are actually accessed. It processes only the necessary access log data required for policy generation, avoiding unnecessary monitoring of all data operations and reducing overall system overhead.
Data Source
AI summary
Systems and methods to establish a time window in which access to a table in a database is monitored, identify every source caller and the information accessed by the caller during the time window, and determine a module access policy (MAP) based on the monitored information to enable column-level encryption. The system may then permit or deny access to information in a column-level encrypted database according to the MAP.