Locally differentially private continuous location sharing with randomized response

Xiong, Xingxing; Liu, Shubo; Li, Dan; Wang, Jun; Niu, Xiaoguang

doi:10.1177/1550147719870379

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…In particular, the method adopts LDP to anonymize location data from the users and reconstructs a synthetic dataset from these perturbed data, and then uses extreme gradient boosting (XGBoost), which is a supervised machine learning approach to conduct the prediction. Xiong et al [130] proposed to apply LDP to private continuous location sharing setting; they introduced a novel variant of LDP to capture the temporal correlations between locations and adopted the GRR mechanism to achieve it for location privacy preservation.…”

Section: Private Location Data Under Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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Section: Private Location Data Under Ldpmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

Self Cite

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“…We note that there has been some work on location data in local settings (i.e., LDP and variants thereof). Chen et al [16] use personalized LDP for spatial data aggregation, Xiong et al [69] focus on continuous location sharing using randomized response, and Cunningham et al [19] publish LDP-compliant sequences of places of interest. However, extensions of these works to our setting are unviable owing to their fundamentally different problem and/or privacy settings.…”

Section: Related Workmentioning

confidence: 99%

GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy

Cunningham¹,

Klemmer²,

Wen³

et al. 2022

Preprint

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Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads to privacy concerns. We introduce GeoPointGAN, a novel GAN-based solution for generating synthetic spatial point datasets with high utility and strong individual level privacy guarantees. GeoPointGAN's architecture includes a novel point transformation generator that learns to project randomly generated point co-ordinates into meaningful synthetic co-ordinates that capture both microscopic (e.g., junctions, squares) and macroscopic (e.g., parks, lakes) geographic features. We provide our privacy guarantees through label local differential privacy, which is more practical than traditional local differential privacy. We seamlessly integrate this level of privacy into GeoPointGAN by augmenting the discriminator to the point level and implementing a randomized response-based mechanism that flips the labels associated with the 'real' and 'fake' points used in training. Extensive experiments show that GeoPointGAN significantly outperforms recent solutions, improving by up to 10 times compared to the most competitive baseline. We also evaluate GeoPointGAN using range, hotspot, and facility location queries, which confirm the practical effectiveness of GeoPointGAN for privacy-preserving querying. The results illustrate that a strong level of privacy is achieved with little-to-no adverse utility cost, which we explain through the generalization and regularization effects that are realized by flipping the labels of the data during training.

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“…To enhance the efficiency of differential privacy schemes, subsequent research has proposed locally deployed differential privacy solutions. For instance, Xiong et al [8] studied random response for continuous location sharing, while Cunningham et al [9] investigated publishing Point of Interest (POI) sequences conforming to local differential privacy.…”

Section: Introductionmentioning

confidence: 99%

A trajectory protection method based on differential privacy and semantic attributes

2024

AJCIS

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With the popularity of smart wearable devices, Location-Based Services (LBS) have been widely applied. However, LBS generates thousands of trajectories, which may potentially leak personal information. To address such privacy concerns, it's often necessary to protect users' trajectory data. Existing differential privacy schemes commonly used for trajectory protection suffer from inefficiency and lack support for semantic attributes. To tackle these issues, this paper proposes a trajectory protection method based on differential privacy and semantic attributes (STrajGAN). Firstly, we employ differential privacy to process the raw data, enhancing efficiency while preserving privacy. Subsequently, the processed data is introduced into a Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU), which generates similar trajectory data while considering the influence of semantic attributes of trajectory points, thus safeguarding user trajectory privacy. Experimental results demonstrate that, compared to other models, STrajGAN can provide better trajectory privacy protection by considering the influence of trajectory point semantic attributes.

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Locally differentially private continuous location sharing with randomized response

Cited by 7 publications

References 32 publications

A Comprehensive Survey on Local Differential Privacy

A Comprehensive Survey on Local Differential Privacy

GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy

A trajectory protection method based on differential privacy and semantic attributes

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