Mobility-Aware Differentially Private Trajectory for Privacy-Preserving Continual Crowdsourcing

Qiu, Guoying; Shen, Yulong

doi:10.1109/access.2021.3058211

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…Kim et al [35] leverages local differential privacy, which is a localized version of differential privacy, to collect indoor positioning data from users, while protecting their location privacy. Con-Crowd-DP [36] is a differentially private framework for mobile crowdsourcing applications in which the mobile users upload the location-related task results to the server for obtaining the rewards. In ConCrowd-DP, to protect of participating users' location privacy, perturbed locations are used instead of users' true locations, when reporting the location-related task results to the server.…”

Section: Plos Onementioning

confidence: 99%

Effectively computing transition patterns with privacy-preserved trajectory datasets

Kim

Jang

2022

PLoS ONE

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Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict and recommend next user locations based on the current position. Thus, in this paper, we explore the computation of the transition patterns, especially with a trajectory dataset collected using differential privacy, which is a de facto standard for privacy-preserving data collection and processing. Specifically, the proposed scheme relies on geo-indistinguishability, which is a variant of the well-known differential privacy, to collect trajectory data from users in a privacy-preserving manner, and exploits the functionality of the expectation-maximization algorithm to precisely estimate hidden transition patterns based on perturbed trajectory datasets collected under geo-indistinguishability. Experimental results using real trajectory datasets confirm that a good estimation of transition pattern can be achieved with the proposed method.

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Section: Plos Onementioning

confidence: 99%

Effectively computing transition patterns with privacy-preserved trajectory datasets

Kim

Jang

2022

PLoS ONE

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“…However, because using the fixed clipping threshold cannot accurately capture the varying characteristic of gradients or parameters, several adaptive methods are proposed to further improve the model utility [19], [20], [31]. Besides directly calibrating the sensitivity, other research with the similar idea has been conducted to improve the tradeoff based on dimension reduction [32], transformation [33], and correlation exploration [13].…”

Section: B Sensitivity Calibrationmentioning

confidence: 99%

“…Recently, differential privacy (DP) has been proposed to protect the privacy of training data by introducing noises to the machine learning process. Machine learning with DP enjoys the high effectiveness and efficiency in privacy preservation, thus attracting increasing attentions from both the academia and industry [10]- [13]. Due to the added noise, DP-based machine learning algorithms often suffer from the model utility degradation and a fundamental problem is how to improve the trade-off between model utility and privacy loss.…”

Section: Introductionmentioning

confidence: 99%

p-Power Exponential Mechanisms for Differentially Private Machine Learning

Ren

Zhao

et al. 2021

IEEE Access

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Differentially private stochastic gradient descent (DP-SGD) that perturbs the clipped gradients is a popular approach for private machine learning. Gaussian mechanism GM, combined with the moments accountant (MA), has demonstrated a much better privacy-utility tradeoff than using the advanced composition theorem. However, it is unclear whether the tradeoff can be further improved by other mechanisms with different noise distributions. To this end, we extend GM (p = 2) to the generalized p-power exponential mechanism (pEM with p > 0) family and show its privacy guarantee. Straightforwardly, we can enhance the privacy-utility tradeoff of GM by searching noise distribution in the wider mechanism space. To implement pEM in practice, we design an effective sampling method and extend MA to pEM for tightly estimating privacy loss. Besides, we formally prove the non-optimality of GM based on the variation method. Numerical experiments validate the properties of pEM and illustrate a comprehensive comparison between pEM and the other two state-of-the-art methods. Experimental results show that pEM is preferred when the noise variance is relatively small to the signal and the dimension is not too high.

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Differentiated Location Privacy Protection in Mobile Communication Services: A Survey from the Semantic Perception Perspective

Qiu,

Tang,

et al. 2023

ACM Comput. Surv.

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Mobile communication services raise users’ privacy concerns in sharing their traveling trajectories while facilitating people’s daily lives. According to these shared trajectories, adversaries can dig users’ multi-modal behavioral semantics by combining with extensive open-source web information. These behavioral semantics raise different levels of privacy concerns to users. We name it the differentiated privacy sensitivity. It makes users have personalized requirements for protecting their travelings. This results in the inevitable evolutionary trend from location privacy protection (LPP) to differentiated location privacy protection (DLPP). DLPP simulates the potential attacks to dig into mobile semantics and characterize the differentiated location sensitivity. It provides the privacy protection with differentiated strength to each location. Differentiated and appropriate strength well balances the tradeoff between privacy protection and data availability for the application QoS. We are motivated to conduct a comprehensive survey on DLPP from the semantic perception perspective. It forms a complete overview of the mobile semantics-aware differentiation in location privacy protection. Specifically, we first review the research works on the multi-modal mobile semantic representation. Then, taking the dug semantics as a clue, we summarize the basic principles of DLPP researches systematically. To complete the overview, we also summarize their design modes and discuss the open opportunities and challenges for future works.

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Mobility-Aware Differentially Private Trajectory for Privacy-Preserving Continual Crowdsourcing

Cited by 3 publications

References 35 publications

Effectively computing transition patterns with privacy-preserved trajectory datasets

Effectively computing transition patterns with privacy-preserved trajectory datasets

p-Power Exponential Mechanisms for Differentially Private Machine Learning

Differentiated Location Privacy Protection in Mobile Communication Services: A Survey from the Semantic Perception Perspective

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