SQL Parsing for Differentially Private Database Queries
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Solution Overview
Problem
Existing techniques for protecting personal data privacy, such as masking, hashing, and anonymization, are resource-intensive and often fail to effectively safeguard individual privacy, while differential privacy technologies face challenges in balancing privacy and utility, particularly in handling untrusted data consumers.
Innovation Solution
Implementing a database system that uses structured language parsing to execute differentially private queries, leveraging a privacy engine to generate differentially private results by applying noise to query results and managing privacy budgets, with support for entity-level and row-level privacy configurations and access controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If masking, hashing, or anonymization techniques are used to protect personal data privacy, then privacy protection is improved, but resource consumption increases and individual privacy safeguarding effectiveness deteriorates
Solution Approach 1:
The patent applies differential privacy by introducing controlled noise parameters into query results. Instead of using traditional masking or hashing, the system modifies the output data by adding calibrated random noise that preserves statistical properties while preventing identification of individual records. This parameter-based approach achieves strong privacy guarantees with lower computational overhead compared to record-level obfuscation techniques.
2Reliability
If differential privacy technologies are used to protect privacy, then privacy protection is improved, but the balance between privacy and data utility deteriorates
Solution Approach 1:
The system dynamically adjusts the noise injection parameters based on the query type, data sensitivity, and privacy budget. By optimizing the noise magnitude and distribution parameters, the patent maintains statistical utility for aggregate analysis while ensuring individual record privacy. The parameter tuning allows flexible balancing act between privacy protection strength and data usefulness for analytical purposes.
Solution Approach 2:
The differential privacy system incorporates feedback mechanisms that monitor privacy budget consumption and adapt noise injection levels accordingly. The system tracks the cumulative privacy expenditure across multiple queries and adjusts subsequent noise parameters to maintain the prescribed privacy guarantees while maximizing data utility. This feedback-driven optimization ensures efficient use of the privacy budget across sequential analytical operations.
3Reliability
If differential privacy is applied to handle untrusted data consumers, then privacy protection is improved, but system complexity increases
Solution Approach 1:
The patent introduces a differential privacy engine as an intermediary layer between the data storage system and query processing system. This dedicated privacy engine handles all privacy-preserving operations, including noise injection, privacy budget management, and query validation. By isolating privacy complexity into a separate modular component, the rest of the system can maintain simpler architecture while still providing robust privacy protection against untrusted data consumers.
Data Source
AI summary
Various example embodiments described herein provide for systems, methods, devices, instructions, and the like for structured language parsing to execute a differentially private query on a database system. According to some example embodiments, a user (e.g., an analyst) submits to a data system (e.g., data platform) a differentially private query using a structured language interface (e.g., SQL interface), which causes the calling of one or more stored procedures on the data system, where the one or more stored procedures encapsulate or facilitate use of a differential privacy engine, which can execute the differentially private query and generate a differentially private query result.