Differentially Private Security Manager Using Gaussian Noise
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Solution Overview
Problem
Existing differential privacy techniques, such as those using the Laplace mechanism, face challenges in accuracy for comparable privacy and require a fixed DP budget schedule, leading to overspending due to the step size.
Innovation Solution
The proposed solution employs a differentially private security manager (DPSM) that uses Gaussian noise and an adaptive step size to minimize privacy budget overspending, while maintaining accuracy through inverse variance weighting and iterative noise calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Laplace mechanism is used for differential privacy, then privacy protection is achieved, but accuracy deteriorates and privacy budget is overspent
Solution Approach 1:
The patent changes the noise distribution parameter from Laplace to Gaussian, and dynamically adjusts the noise scale parameter based on the sensitivity of the query and remaining privacy budget. This allows optimizing the balance between privacy protection and accuracy by tuning these parameters rather than using fixed Laplace noise
Solution Approach 2:
The patent introduces dynamic adjustment of noise parameters based on the current state of privacy budget and query characteristics. The noise scale is adapted dynamically during the process rather than being fixed, allowing the system to respond to changing conditions and optimize both privacy and accuracy
2Ease of manufacture
If fixed DP budget schedule is used, then implementation is simple, but privacy budget is overspent
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the privacy budget consumption and adjusts the noise parameters accordingly. The privacy spend is tracked and fed back into the system to modify subsequent noise additions, preventing overspending while maintaining simple overall structure
Solution Approach 2:
The system automatically adjusts its own noise parameters based on monitored privacy budget state without requiring external intervention. The differential privacy mechanism self-regulates to optimize budget usage through internal feedback loops
Data Source
AI summary
Example differential privacy techniques include receiving a request to perform a query on a set of data stored by a database. The request identifies a target accuracy and a maximum privacy spend. The target accuracy includes a maximum relative error. The maximum privacy spend includes a value of a zero-concentrated privacy parameter ρ associated with a degree of information released about the set of data due to the query. A differentially private count operation is performed on the set of data to produce a differentially private result. The differentially private count operation includes performing a count operation on data to produce a result and perturbing the result to produce a differentially private result using a noise value sampled from a Gaussian distribution and based on a fractional privacy spend comprising a fraction of the maximum privacy spend. The differentially private result is encoded for transmission to the client device.