A differential privacy protection method based on salient point privacy budget redistribution

By identifying salient and non-salient points in the differential privacy method, reallocating the privacy budget, and performing local differential privacy perturbations, the problems of resource waste and low data analysis accuracy in existing technologies are solved, achieving more efficient privacy protection and data utilization.

CN121997376BActive Publication Date: 2026-07-03CHONGQING UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHONGQING UNIV
Filing Date
2026-02-02
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing differential privacy methods ignore the temporal differences in data importance when processing streaming data. This results in the privacy budget being evenly consumed on ordinary data points with limited information value, leading to resource waste. Furthermore, when critical data points appear, insufficient available budget in certain areas causes information to be masked by noise, affecting the accuracy of data analysis.

Method used

By acquiring users' time series data, we divide the sliding time window and identify salient and non-salient points. We reclaim the privacy budget of non-salient points and reallocate it to salient points. We then use the updated privacy budget to perform local differential privacy perturbation on the salient point data and reconstruct the complete time series data using the least squares method.

Benefits of technology

This approach achieves the goal of meeting differential privacy protection requirements while improving the accuracy of time series data analysis and the rationality of privacy budgets, reducing resource waste, and ensuring privacy protection for key data points.

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Abstract

This invention relates to the field of differential privacy technology, and discloses a differential privacy protection method, apparatus, and device based on salient point privacy budget reallocation. The method includes: acquiring a user's time-series data and presetting a sliding time window size and a window-level privacy budget; dividing the time-series data into multiple sliding time windows; uniformly distributing the window-level privacy budget to each time point within the sliding time window; identifying salient and insignificant points within each sliding time window; reclaiming the initial privacy budget corresponding to the insignificant points and allocating the reclaimed initial privacy budget to the nearest salient point after the time point of the insignificant point, and updating the privacy budget of the salient points; generating perturbed salient point data using the updated privacy budget of the salient points; and reconstructing the complete time-series data using the least squares method based on the perturbed salient point data. This invention ensures that the reconstructed complete time-series data meets the requirements of differential privacy protection.
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