Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces

Gürsoy, Mehmet Emre; Liu, Ling; Truex, Stacey; Yu, Lei; Wei, Wüchang

doi:10.1145/3243734.3243741

Cited by 78 publications

(70 citation statements)

References 59 publications

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“…Moreover, the problem with the trusted curator model adopted in these methods is its vulnerability to privacy attacks in which an adversary accesses the data gathered by the data curator before the data obfuscation takes place [34] . Several works ( [82], [83], and [84]) show that a well designed perturbation schema can reduce the regularity and uniqueness of any individual users' trajectory RQ3. Therefore the presented methods are robust against attacks that exploit the user's movement regularity.…”

Section: Sliding Window Methodsmentioning

confidence: 99%

An Analysis of Differential Privacy Research in Location and Trajectory Data

Errounda

Liu²

2020

Preprint

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Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

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Section: Sliding Window Methodsmentioning

confidence: 99%

An Analysis of Differential Privacy Research in Location and Trajectory Data

Errounda

Liu²

2020

Preprint

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“…Allocating unnecessary high privacy budgets to such private operation phases will negatively affect location accuracy because it would steal from those private operation phases that do need high privacy budgets to remain accurate. One way for further optimizing the allocation strategy of privacy budget is to use regression-based learning on e [37]. This is a plan of our future work.…”

Section: Theoremmentioning

confidence: 99%

A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing

Zhang

Qian

et al. 2022

Neural Comput & Applic

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As an enabling technology for edge computing scenarios, indoor localization has a broad prospect in a variety of location-based applications, such as tracking, navigating, and monitoring in indoor environments. In order to improve the location accuracy, numerous machine learning (ML)-based indoor localization schemes with fingerprint fusion have been proposed recently, which take advantage of the fusion of signal gathered from multiple wireless technologies (e.g., WiFi and BLE) and require a site survey to construct the fingerprint database. However, most solutions are based on cloud framework and thus pose a serious privacy leakage because users’ sensitive information (e.g., locations) is computed from the fingerprint database by the untrusted localization service provider. Furthermore, the site survey is time-consuming and labor-intensive. In this paper, we propose a differentially private fingerprint fusion semi-supervised extreme learning machine for indoor localization in the edge computing, called Adp-FSELM. The Adp-FSELM firstly employs a multi-level edge network-based privacy-preserving system framework to meet the requirements of ML-based fingerprint indoor localization for lightweight, low latency, and real-time response. Then, the Adp-FSELM extends the $$\varepsilon$$ ε -differential privacy to the fingerprint fusion semi-supervised extreme learning machine for indoor localization in edge computing through a three-phase private process consisting of private labeled sample obfuscation, differentially private feature fusion, and differentially private model training. Theoretical and comprehensive experimental results in real indoor environments demonstrate that the Adp-FSELM provides a high $$\varepsilon$$ ε -differential privacy guarantee for users’ location privacy while reducing human calibration effort and effectively resists Bayesian inference attacks. Compared with the existing semi-supervised learning-based localization methods, the mean absolute error of location accuracy of the Adp-FSELM is restricted to 2.22% at most, and the additional time consumption can be almost ignored. Thus, our mechanism can balance the trade-off among location privacy, location accuracy, and time consumption.

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“…Modeling users' mobile behaviors in the mobile applications is an open issue [15], [21]. Literature [22] proposed a utilityaware synthesis of DP trace, which took the trip's distribution, length and start-end points into consideration in the usability perception. Li et al studied the users' mobilitymodeling issues in the mobile social network [23].…”

Section: A Spatiotemporal Mobility Perceptionmentioning

confidence: 99%

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

Qiu

Shen

2021

IEEE Access

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Participating in mobile services by synthesizing trajectories with consistent lifestyle and meaningful mobility as actual traces are the most popular way to protect location privacy. However, recent trajectory synthesizing techniques are still threatened by the information that the attacker inevitably obtains, such as the locations of the accepted tasks in the crowdsourcing application. With this information and the spatiotemporal correlation hidden in the user's mobility, the attacker can infer the user's actual location and even future behaviors. It remains open to defend against such inferential attacks in the continual crowdsourcing scenarios. In this paper, we propose a mobility-aware differentially private solution, ConCrowd-DP, for achieving the privacy-preserving continual crowdsourcing application. Specifically, before starting the application, we first construct a spatiotemporal mobile model, STMarkov, to model the spatiotemporal correlation in users' mobility. Then, a perturbed location is generated for the user to participate in the crowdsourcing application, according to STMarkov and K-norm DP. Finally, we eliminate the privacy threat brought by the accepted task based on K-norm DP and Bayesian posterior theorem. With ConCrowd-DP in place, a mobilityaware differentially private trace is generated for the user to participate in the application continually. Extensive experiments with real-world datasets demonstrate that ConCrowd-DP guarantees the usability of the synthesized trajectory effectively, while providing the DP protection for defending against the inferential attacks which stem from the multiple accepted tasks.

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Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces

Cited by 78 publications

References 59 publications

An Analysis of Differential Privacy Research in Location and Trajectory Data

An Analysis of Differential Privacy Research in Location and Trajectory Data

A differentially private indoor localization scheme with fusion of WiFi and bluetooth fingerprints in edge computing

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

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