Query Optimizer Security Scoring for Sensitive Database Tables
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Solution Overview
Problem
Existing databases face challenges in securely managing sensitive data, as malicious actors can exploit legitimate access credentials to obtain sensitive information by injecting malicious code or issuing broad queries that return large amounts of sensitive data.
Innovation Solution
Implementing a query optimizer that computes a security score for queries targeting sensitive data, restricting access if the score exceeds a security threshold, by analyzing query attributes such as source, specificity, and access methods to prevent malicious attempts to obtain sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If broad queries are allowed to access sensitive data, then data accessibility is improved, but security is worsened
Solution Approach 1:
The system changes the parameter of query evaluation by introducing a security score metric. Instead of simply allowing or denying access based on basic authentication, the system computes a security score for each query based on multiple attributes (source, specificity, access method) and compares it against a threshold. This parameter transformation enables nuanced control that balances accessibility and security.
Solution Approach 2:
The system implements feedback by continuously monitoring query attributes and adjusting access decisions based on computed security scores. The security scoring mechanism provides feedback about the risk level of each query, enabling dynamic access control that responds to the specific characteristics of each access attempt rather than applying static rules.
2Object-affected harmful factors
If security scoring and query analysis are implemented, then security is improved, but system complexity is worsened
Solution Approach 1:
The system segments the security evaluation process into distinct components: identifying secure tables, analyzing query attributes (source, specificity, access method), computing security scores, and making access decisions. This segmentation allows each component to be independently implemented and optimized, reducing overall system complexity despite the enhanced security functionality.
Solution Approach 2:
The system performs preliminary action by pre-identifying secure tables and pre-defining security thresholds before queries are executed. The query optimizer is configured in advance with knowledge of which tables are sensitive and what security scores are acceptable, allowing security evaluation to be integrated into the normal query processing flow without requiring complex runtime decisions about which tables need protection.
3Reliability
If security scores are computed for all queries, then security monitoring is improved, but processing time is worsened
Solution Approach 1:
The system applies local quality by computing security scores only for queries that access secure tables, rather than evaluating all queries in the database. The query optimizer identifies which tables are marked as secure and applies security scoring only to queries targeting those specific tables, leaving other queries to be processed through normal optimization paths without additional security overhead.
Data Source
AI summary
Systems, methods, and computer program products to perform an operation comprising upon determining that a received query requests values of sensitive data stored in a secure database table of a database, computing a security score for the received query based on a determined specificity of a selection predicate of the received query, and upon determining that the security score exceeds a security threshold, performing a predefined operation to restrict access to the requested values of the sensitive data.