Anonymizing Sensitive Data in Logic Problems for Constraint Solvers
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Solution Overview
Problem
Constraint solvers struggle to effectively manage complex security policies in computing resource service providers due to their difficulty in handling sophisticated academic computation theories and limited configurability, leading to challenges in validating structured documents and ensuring secure network configurations.
Innovation Solution
A system that automatically transforms structured documents, such as security policies, into logic problems, anonymizes sensitive data while preserving document structure, and generates a dataset of example problems for constraint solver developers to refine the solver's ability to evaluate the validity of structured documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If constraint solvers use sophisticated academic computation theories to validate security policies, then validation accuracy is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent introduces an intermediary translation layer that converts security policies into constraint satisfaction problems (CSP). This intermediary representation layer allows the complex validation logic to be encapsulated in a standardized CSP format, making the solver more usable without sacrificing validation accuracy. The translation layer acts as a mediator between the user-friendly policy input and the sophisticated constraint solving engine.
Solution Approach 2:
The patent segments the security policy validation process into distinct components: policy parsing, constraint generation, and constraint solving. By dividing the complex validation task into manageable segments, each component can be optimized independently. The constraint generation module transforms policy rules into structured constraints, while the solver focuses purely on finding satisfying assignments, thereby reducing overall system complexity.
2Productivity
If constraint solvers process more code examples to improve problem-solving ability, then productivity is improved, but loss of time for training and setup increases
Solution Approach 1:
The patent performs preliminary action by pre-defining constraint templates and validation rules for common security policy patterns. Instead of requiring the solver to learn from scratch through extensive training examples, the system pre-configures the constraint generation logic with knowledge about typical security policy structures. This preliminary setup enables the solver to immediately handle new policies without requiring time-consuming training.
Solution Approach 2:
The patent uses copying by creating reusable constraint templates from exemplar security policies. Once constraint patterns are established for common policy types, these templates can be copied and adapted to new policies, eliminating the need to process every possible code example from scratch. The system learns from a limited set of representative examples and generalizes through template replication.
3Reliability
If security policies are made more comprehensive to cover expanding users and resources, then reliability is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent applies parameter changes by transforming security policies into a different representation space - from natural language or configuration files to formal constraint satisfaction problems. This parameter transformation allows comprehensive security policies to be expressed in a standardized mathematical format that the constraint solver can process efficiently. The complexity is shifted from the policy language to the constraint representation, which the solver handles systematically.
Solution Approach 2:
The patent substitutes manual policy management and validation with an automated constraint solving mechanism. Instead of relying on manual review or simple rule-based validation, the system uses formal constraint satisfaction theory to automatically verify security policies. This mechanical substitution of automated solving replaces complex human analysis with systematic computational validation.
4Loss of information
If sensitive data is anonymized in logic problems, then loss of information is reduced, but measurement precision of sensitive data deteriorates
Solution Approach 1:
The patent extracts sensitive data from the original security policies and separates it into anonymized placeholder variables. By taking out the sensitive information and replacing it with generic constraint variables, the system preserves the structural relationships and logical constraints while removing identifying information. The extracted sensitive data can be handled separately through appropriate security measures.
Solution Approach 2:
The patent creates anonymized copies of security policies for use as training data. These copies maintain the structural integrity and logical relationships of the original policies but replace sensitive values with anonymized placeholders. The copying process preserves the satisfiability and constraint structure while eliminating sensitive information, enabling safe use in solver training and development.
Data Source
AI summary
A document anonymization system transforms structured documents, such as security policies, that contain user-specific and other sensitive data, producing encoded logic problems in the format or language of one or more constraint solvers; the logic problems do not contain any of the sensitive data. The system may perform a one- or two-stage anonymization process: in a first stage, the electronic document is analyzed according to its document type to identify parameters likely to contain sensitive data, and the associated values are replaced with arbitrary values; in a second stage, after the anonymized electronic document is converted into logic formulae representing the data, the system performs replacements of string constants in the logic formulae with arbitrary strings to further anonymize the sensitive data. The system may confirm that anonymization preserves the document structure, difficulty level, and satisfiability of the original document by executing the constraint solver against the anonymized logic problem.