Database Inference Attack Detection Across Multiple DBMSs
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Solution Overview
Problem
Existing technologies fail to detect and prevent database inference attacks, particularly those involving access control and query control methods, which are difficult to detect and prevent inference attacks, and are not effective in detecting inference attacks, and are not effective in preventing information leakage caused by inference attacks across multiple databases.
Innovation Solution
An apparatus and method for controlling database inference attacks by receiving result data from database management systems, detecting inference attacks using inference knowledge, restricting data attributes corresponding to inference attack elements, and providing restricted data to prevent sensitive information from being inferred.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access control and query control methods are applied to protect database information, then security against direct access is improved, but detection capability against inference attacks deteriorates
Solution Approach 1:
An inference attack detection unit is introduced as an intermediary component between the query control unit and the database management system. This detection unit monitors and analyzes query patterns to identify inference attacks, while the query control unit continues to enforce access control rules. The intermediary detection capability enables the system to maintain security control while detecting sophisticated inference attacks that bypass traditional access control mechanisms.
2Reliability
If all information that may be used for inference attacks is de-identified or encrypted, then prevention of inference attacks is improved, but availability of information for legitimate use deteriorates
Solution Approach 1:
The system applies different levels of protection and processing to different data attributes based on their sensitivity and risk characteristics. The inference attack detection unit analyzes query patterns and identifies specific attributes that are being targeted for inference attacks. Rather than encrypting or de-identifying all data, the system selectively restricts only those attributes involved in inference attacks, thereby maintaining data availability for legitimate uses while preventing inference attacks.
Solution Approach 2:
The system dynamically adjusts data attribute restrictions based on real-time detection of inference attacks. The query control unit receives feedback from the inference attack detection unit and modifies which data attributes are restricted in subsequent queries. This dynamic adaptation allows the system to maintain high data availability during normal operations while rapidly responding to and preventing inference attacks when detected.
3Difficulty of detecting and measuring
If inference attack detection and restriction mechanisms are added to the database system, then detection capability against inference attacks is improved, but system complexity deteriorates
Solution Approach 1:
The query control unit is designed to perform multiple functions: it enforces access control rules, receives and processes inference attack detection results, and dynamically restricts data attributes based on detected attacks. By making the query control unit multi-functional, the system avoids adding separate complex components and instead enhances the existing query control mechanism to handle both access control and inference attack prevention, thereby reducing overall system complexity.
4Difficulty of detecting and measuring
If result data from multiple database management systems is analyzed for inference attacks, then detection capability across multiple databases is improved, but processing time deteriorates
Solution Approach 1:
The system pre-establishes inference knowledge bases containing inference rules, attack patterns, and relationships between data attributes before inference attacks occur. The inference attack detection unit utilizes these pre-prepared knowledge bases to rapidly analyze query patterns and result data from multiple database management systems. By having detection rules and knowledge ready in advance, the system can perform cross-database inference attack detection without excessive processing delays.
Data Source
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AI summary
The present disclosure relates to an apparatus and a method for controlling database (DB) inference attacks and, more specifically, to an apparatus and a method for controlling DB inference attacks, in which result data output from multiple database management systems (DBMSs) managing one or more DBs is received to detect inference attacks, which are difficult to detect using access control and query control methods, based on preestablished inference knowledge. The present disclosure provides an apparatus and a method for analyzing inference attacks based on multi-level risk, restricting one or more data attributes corresponding to inference attack elements associated with the inference attack, and providing the restricted result data to an inquirer, thereby preventing sensitive information from being inferred and leaked.