Differentially Private Event Sequences over Infinite Streams with Relaxed Privacy Guarantee

Ren, Xuebin; Wang, Shuyang; Yao, Xianghua; Yu, Chia Mu; Yu, Wei; Yang, Xinyu

doi:10.1007/978-3-030-23597-0_22

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…To bridge the gap between event-level privacy and user-level privacy, Kellaris et al [11] propose the w-event differential privacy and its implementation algorithm, BD algorithm. Ren et al [23] propose the average w-event differential privacy to relax the requirement of privacy budget consumed in w timestamps. e algorithm in [10] protects the finite data series on each place separately.…”

Section: Related Workmentioning

confidence: 99%

Differentially Private Web Browsing Trajectory over Infinite Streams

Liu

Guo

Tan

et al. 2021

Security and Communication Networks

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Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

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Section: Related Workmentioning

confidence: 99%

Differentially Private Web Browsing Trajectory over Infinite Streams

Liu

Guo

Tan

et al. 2021

Security and Communication Networks

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“…It ensures that the outcomes of any analyses on neighboring datasets (i.e., two datasets that have only one data difference) are difficult to distinguish. Based on differential privacy, a lot of varietal schemes have been proposed for privacy protection [15][16][17][18][19][20][21][22]. However, most of them focus on either the user-level privacy on finite streams or the event-level privacy on infinite streams.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Real-Time Traffic Data Sharing: A Differential Privacy-Based Scheme with Spatial Correlation

Le,

Xing,

Zhang

et al. 2024

Mathematics

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The real-time sharing of traffic data can offer improved services to users and timely respond to environmental changes. However, this data often involves individuals’ sensitive information, raising substantial privacy concerns. It is imperative to find ways to protect the privacy of the shared traffic data while maintaining its ongoing data utility. In this paper, a Differential Privacy-based scheme with Spatial Correlation for Real-time traffic data (named as DP-SCR) is proposed. DP-SCR not only ensures the high data utility of shared traffic data, but also provides strong privacy protection. Specifically, DP-SCR is designed to adhere to w-event ε-differential privacy, ensuring a high level of privacy protection. Subsequently, a novel adaptive allocation based on spatial correlation prediction is proposed to optimize the privacy budget allocation in differential privacy. In addition, a feasible dynamic clustering algorithm is developed to minimize the relative perturbation error, which further improves the quality of shared data. Finally, the analyses demonstrate that DP-SCR provides w-event privacy for the shared data of each section, and the spatial correlation is a more pronounced characteristic of the traffic data than other characteristics. Meanwhile, experiments conducted on real-world data show that the MAR and MER of the predicted data in DP-SCR are smaller than those in other baseline DP-based schemes. It indicates that the DP-SCR scheme proposed in this paper can provide more accurate shared data.

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Data Correlation behaviour on Privacy Leakage in Differential Privacy

2023

2023 International Conference on Computer, Electronics &Amp; Electrical Engineering &Amp; Their Applications (IC2E3)

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Differentially Private Event Sequences over Infinite Streams with Relaxed Privacy Guarantee

Cited by 5 publications

References 13 publications

Differentially Private Web Browsing Trajectory over Infinite Streams

Differentially Private Web Browsing Trajectory over Infinite Streams

Enhancing Real-Time Traffic Data Sharing: A Differential Privacy-Based Scheme with Spatial Correlation

Data Correlation behaviour on Privacy Leakage in Differential Privacy

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