An Analysis of Differential Privacy Research in Location Data

Errounda, Fatima Zahra; Liu, Yan

doi:10.1109/bigdatasecurity-hpsc-ids.2019.00021

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…One of the popular mechanisms to achieve DP perturb the original query result using random noise that is calibrated with the privacy budget ϵ and defines a global sensitivity for all neighbouring D and D ′ [15]. The work in [17] reviews research works done in differential privacy targeted toward location data from a data flow perspective, including collection, aggregation, and mining. [2] proposed a Geo-indistinguishability notion based on differential privacy and a planar Laplace mechanism.…”

Section: Related Work 21 Notions Of Location Privacymentioning

confidence: 99%

Privacy-Aware Adversarial Network in Human Mobility Prediction

Zhan

Haddadi

Mashhadi

2023

PoPETs

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As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).

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Section: Related Work 21 Notions Of Location Privacymentioning

confidence: 99%

Privacy-Aware Adversarial Network in Human Mobility Prediction

Zhan

Haddadi

Mashhadi

2023

PoPETs

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“…We will compare with the geometric progression 47 scheme proposed by Errounda et al and the noise trajectory segment prefix tree 36 and trajectory length tree based on differential privacy 48 schemes proposed by Zhao et al and verify from three aspects: time efficiency, data accuracy, and data privacy.…”

Section: Analysis and Evaluationmentioning

confidence: 99%

Secure storage scheme of trajectory data for digital tracking mechanism

Wang

et al. 2022

Int J of Intelligent Sys

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The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four‐dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.

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“…In our previous work [15], we covered only the location privacy aspect. Here, we extend our work to trajectory privacy as well.…”

Section: ) Identify Research Gaps and Offer Future Research Directionsmentioning

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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An Analysis of Differential Privacy Research in Location Data

Cited by 4 publications

References 32 publications

Privacy-Aware Adversarial Network in Human Mobility Prediction

Privacy-Aware Adversarial Network in Human Mobility Prediction

Secure storage scheme of trajectory data for digital tracking mechanism

An Analysis of Differential Privacy Research in Location and Trajectory Data

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