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arXiv:2611.11467 · v1

PLDP-IDS: Personalized Local Differential Privacy for Infinite Data Stream

Leilei DuYikun HuXu ZhouYang CaoPeng ChengWangze NiKenli Li
cs.DB
首次提交 2026-11-09 00:00
版本更新 2026-11-09 00:00
雷达观测 2026-11-10 12:16
arXiv ↗PDF ↗

摘要

Streaming data collection under local differential privacy (LDP) enables real-time analytics without a trusted curator. Existing ww-event LDP mechanisms, however, impose a uniform privacy window and budget on all users, forcing everyone to adopt the most stringent requirement. We study personalized continual frequency estimation under LDP, where each user specifies her own window size and privacy budget, and identify two challenges. First, a single sampling size cannot balance sampling and perturbation errors for privacy groups with heterogeneous requirements. Second, borrowing estimates from groups with larger budgets reduces variance but introduces bias when privacy preferences correlate with the data. We first propose Optimal Population Selection (OPS) and two OPS-guided mechanisms, PLPD and PLPA. We then develop PLPD+ and PLPA+, which combine group-aware population scheduling with bias-aware cross-group fusion while preserving personalized w-event LDP. Experiments on real-world and synthetic streams show that PLPA+ reduces the AMSE of the direct personalized extension of LDP-IDS by 52.3% and 40.8% on average, respectively, and remains about 5×5\times more accurate than PLPA under strong privacy--data correlation.

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摘要变化

Streaming data collectionExisting under$w$-event local differential privacy (LDP) enables real-time analytics without a trusted curator. Existing w-eventmechanisms LDPfor mechanisms,streaming however,data impose a uniform privacy window and budget, onforcing all users, forcing everyone to adopt the most stringent privacy requirement. We study personalized continual frequency estimation under LDP, where each user specifies her own window size and privacy budget, and identify two challenges. First, a single sampling size cannot balance sampling and perturbation errors for privacy groups with heterogeneous requirements. Second, borrowing estimates from groups with larger budgets reduces variance but introduces bias when privacy preferences correlate with the data. We first propose Optimal Population Selection (OPS) and two OPS-guided mechanisms, PLPD and PLPA, which use one privacy-compatible sampling size for all users. WeSince thenone size cannot fit all groups and pooling their estimates is biased under privacy--data correlation, we further develop PLPD$^+$ and PLPA$^+$, which combine group-aware population scheduling with bias-aware cross-group fusion while preserving personalized $w$-event LDP. Experiments on real-world and synthetic streams show that PLPA+ reduces the, AMSEcompared ofwith the direct personalized extension of LDP-IDS, PLPA$^+$ reduces the average mean squared error (AMSE) by 52.3\% and 40.8\% on average, respectively, and remains about $4.3--5.1$\times$ more accurate than PLPA under strong privacy--data correlation.

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v3 · 2026-11-13 01:56 · 最新观测版本

PLDP-IDS: Personalized Local Differential Privacy for Infinite Data Stream

Existing ww-event local differential privacy (LDP) mechanisms for streaming data impose a uniform privacy window and budget, forcing all users to adopt the most stringent privacy requirement. We study personalized continual frequency estimation under LDP, where each user specifies her own window size and privacy budget, and identify two challenges. First, a single sampling size cannot balance sampling and perturbation errors for privacy groups with heterogeneous requirements. Second, borrowing estimates from groups with larger budgets reduces variance but introduces bias when privacy preferences correlate with the data. We first propose Optimal Population Selection (OPS) and two OPS-guided mechanisms, PLPD and PLPA, which use one privacy-compatible sampling size for all users. Since one size cannot fit all groups and pooling their estimates is biased under privacy--data correlation, we further develop PLPD+^+ and PLPA+^+, which combine group-aware population scheduling with bias-aware cross-group fusion while preserving personalized ww-event LDP. Experiments on real-world and synthetic streams show that, compared with the direct personalized extension of LDP-IDS, PLPA+^+ reduces the average mean squared error (AMSE) by 52.3\% and 40.8\% on average, respectively, and remains 4.3--5.1×\times more accurate than PLPA under strong privacy--data correlation.

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v2 · 2026-11-10 17:31

PLDP-IDS: Personalized Local Differential Privacy for Infinite Data Stream

Streaming data collection under local differential privacy (LDP) enables real-time analytics without a trusted curator. Existing w-event LDP mechanisms, however, impose a uniform privacy window and budget on all users, forcing everyone to adopt the most stringent requirement. We study personalized continual frequency estimation under LDP, where each user specifies her own window size and privacy budget, and identify two challenges. First, a single sampling size cannot balance sampling and perturbation errors for privacy groups with heterogeneous requirements. Second, borrowing estimates from groups with larger budgets reduces variance but introduces bias when privacy preferences correlate with the data. We first propose Optimal Population Selection (OPS) and two OPS-guided mechanisms, PLPD and PLPA. We then develop PLPD+ and PLPA+, which combine group-aware population scheduling with bias-aware cross-group fusion while preserving personalized w-event LDP. Experiments on real-world and synthetic streams show that PLPA+ reduces the AMSE of the direct personalized extension of LDP-IDS by 52.3% and 40.8% on average, respectively, and remains about 5×5\times more accurate than PLPA under strong privacy--data correlation.

查看此版本解读 →查看此版本原文 ↗
v1 · 2026-11-09 00:00

PLDP-IDS: Personalized Local Differential Privacy for Infinite Data Stream

Streaming data collection under local differential privacy (LDP) enables real-time analytics without a trusted curator. Existing ww-event LDP mechanisms, however, impose a uniform privacy window and budget on all users, forcing everyone to adopt the most stringent requirement. We study personalized continual frequency estimation under LDP, where each user specifies her own window size and privacy budget, and identify two challenges. First, a single sampling size cannot balance sampling and perturbation errors for privacy groups with heterogeneous requirements. Second, borrowing estimates from groups with larger budgets reduces variance but introduces bias when privacy preferences correlate with the data. We first propose Optimal Population Selection (OPS) and two OPS-guided mechanisms, PLPD and PLPA. We then develop PLPD+ and PLPA+, which combine group-aware population scheduling with bias-aware cross-group fusion while preserving personalized w-event LDP. Experiments on real-world and synthetic streams show that PLPA+ reduces the AMSE of the direct personalized extension of LDP-IDS by 52.3% and 40.8% on average, respectively, and remains about 5×5\times more accurate than PLPA under strong privacy--data correlation.

查看此版本解读 →查看此版本原文 ↗