Differentially Private Database Query System with Privacy Budget Control
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Solution Overview
Problem
Existing techniques for analyzing restricted personal data, such as differential privacy, often compromise analytical utility and lack fine-grained control, making it difficult to extract maximum value from sensitive data while ensuring privacy protection.
Innovation Solution
A differentially private security system that communicates with a database, receives queries, and applies differential privacy to ensure that the results are (ε,δ)-differentially private, allowing for fine-grained control over privacy parameters and budget, thereby balancing privacy protection with data utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy is applied to protect restricted data, then privacy protection is improved, but analytical utility is compromised
Solution Approach 1:
The patent applies parameter changes by adjusting the privacy budget parameter ε (epsilon) to control the trade-off between privacy protection and analytical utility. By allowing flexible adjustment of ε and other differential privacy parameters, the system can optimize the balance between protecting restricted data and maintaining data usefulness for analysis.
Solution Approach 2:
The system implements dynamics by enabling fine-grained control over privacy parameters and budget allocation. Different queries can have different privacy budgets assigned, and the system can dynamically adjust privacy levels based on query sensitivity, data type, and analytical requirements, rather than applying a static privacy level to all operations.
2Reliability
If access controls are used to restrict database access, then security is improved, but data accessibility is reduced
Solution Approach 1:
The patent introduces an intermediary layer that sits between the database and users. This intermediary applies differential privacy mechanisms to query results, allowing broader access to data while protecting sensitive information. Instead of restricting access at the data level, the system protects privacy at the output level, enabling more users to access data with appropriate privacy guarantees.
3Reliability
If data masking is applied to remove personally-identifiable information, then privacy is improved, but statistical properties are compromised
Solution Approach 1:
The patent replaces mechanical data masking techniques with a mathematical approach based on differential privacy. Instead of removing or distorting data through masking, the system uses carefully calibrated noise addition and probabilistic mechanisms that preserve statistical properties while providing provable privacy guarantees. This substitution maintains both privacy and statistical integrity.
Data Source
AI summary
A differentially private security system is communicatively coupled to a database. The differentially private security system receives a request from a client device to perform a query of the database and identifies a level of differential privacy corresponding to the request. The identified level of differential privacy includes privacy parameters (ε,δ) indicating the degree of information released about the database. The differentially private security system performs a differentially private query upon a set of data in the database such that the performance of the query produces a result that is (ε,δ)-differentially private.