Local Differential Privacy Tuning for Targeted Frequency Estimation
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Solution Overview
Problem
Existing data transmission methods lack effective mechanisms to reduce variance in frequency estimation for targeted frequency ranges, compromising the utility of data received by recipient processing systems due to privacy concerns and security vulnerabilities.
Innovation Solution
Implementing local differential privacy (LDP) operations at the client device level, including hash functions, noise addition, and probabilistic data inclusion, to generate encrypted output vectors that are transmitted to a recipient processing system, which adjusts mechanism parameters to target specific frequency ranges and reduce variance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If local differential privacy operations are applied to data before transmission, then data security and privacy are improved, but variance in frequency estimation increases and utility of received data deteriorates
Solution Approach 1:
The patent applies parameter changes by adjusting the noise addition mechanism and probabilistic inclusion parameters dynamically based on the frequency range of interest. For targeted frequency ranges, the system modifies the LDP mechanism parameters (such as noise intensity and inclusion probability) to reduce variance in frequency estimation while maintaining privacy protection. This allows optimization of the trade-off between security and measurement precision for specific applications.
2Object-affected harmful factors
If probabilistic data inclusion is used in LDP, then data privacy is enhanced, but variance in received data increases reducing utility
Solution Approach 1:
The patent implements local quality by applying different probabilistic inclusion parameters to different frequency ranges. Instead of using a uniform inclusion probability for all data, the system tailors the inclusion parameters locally to the specific frequency range being analyzed. This allows high-frequency ranges to use different parameters than low-frequency ranges, optimizing data utility for each segment while maintaining overall privacy protection.
3Measurement precision
If mechanism parameters are adjusted to reduce variance for targeted frequency ranges, then frequency estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the frequency spectrum into multiple ranges and applying different LDP mechanism parameters to each segment. The system segments the frequency data and processes each segment with optimized parameters tailored to its characteristics. This segmentation approach reduces variance for targeted ranges while managing complexity through systematic organization of the parameter adjustment process.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reducing variance for target frequency ranges. One of the methods includes sending a request for data to each of a plurality of user devices; determining a target frequency range for the requested data; computing a value for an inclusion probability according to the target frequency range; providing the value for the inclusion probability to each of the plurality of user devices; receiving privatized messages from each of the plurality of user devices, each privatized message being generated according to the provided value for the inclusion probability; and analyzing the privatized data extracted from the received messages.