Real-time and private spatio-temporal data aggregation with local differential privacy

Xiong, Xingxing; Liu, Shubo; Li, Dan; Cai, Zheren; Niu, Xiaoguang

doi:10.1016/j.jisa.2020.102633

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…Lan et al [25] proposed personalized differential privacy (iDP-SC) based on a spectral clustering algorithm to reduce the local sensitivity by the introduction of the spectral clustering algorithm, and the noise reduction generated by spectral clustering compensates for the information distortion error introduced by itself. Xiong et al [26] proposed a new (ε, δ)-LDP concept for capturing users' privacy needs by accounting for the temporal relevance of spatiotemporal data at the same time as guaranteeing sensible utility, demonstrating its superiority in achieving a better trade-off between privacy and utility for real-time spatiotemporal data integration and rigorous privacy protection.…”

Section: Related Workmentioning

confidence: 99%

Support Personalized Weighted Local Differential Privacy Skyline Query

Zhang

Chen

Jia

et al. 2022

Security and Communication Networks

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The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Support Personalized Weighted Local Differential Privacy Skyline Query

Zhang

Chen

Jia

et al. 2022

Security and Communication Networks

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“…Hong et al [8] also investigate the problem of collecting user location data and propose a perturbation mechanism to reduce the error of each collected location data. Finally, authors in [23] investigate the privacy concerns of real-time spatio-temporal data aggregation and suggests using LDP-based algorithms. Publishing and sharing location statistics are also proposed in [5] employing LDP to ensure users' privacy while continuously releasing statistics over infinite streams.…”

Section: Related Workmentioning

confidence: 99%

Hide me Behind the Noise: Local Differential Privacy for Indoor Location Privacy

Navidan,

Moghtadaiee,

Nazaran

et al. 2022

Preprint

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The advent of numerous indoor location-based services (LBSs) and the widespread use of many types of mobile devices in indoor environments have resulted in generating a massive amount of people's location data. While geo-spatial data contains sensitive information about personal activities, collecting it in its raw form may lead to the leak of personal information relating to the people, violating their privacy. This paper proposes a novel privacy-aware framework for aggregating the indoor location data employing the Local Differential Privacy (LDP) technique, in which the user location data is changed locally in the user's device and is sent to the aggregator afterward. Therefore, the users' locations are kept hidden from a server or any attackers. The practical feasibility of applying the proposed framework is verified by two real-world datasets. The impact of dataset properties, the privacy mechanisms, and the privacy level on our framework are also investigated. The experimental results indicate that the presented framework can protect the location information of users, and the accuracy of the population frequency of different zones in the indoor area is close to that of the original population frequency with no knowledge about the location of people indoors.

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“…However, existing research on location differential privacy protection still suffers from the following problems; existing location differential protection mechanisms are only effective for single or sporadic queries, but in the case of multiple queries, the user's true location may still be exposed [4] .This is due to the spatiotemporal correlation between successive queried locations, and thus the query not only leaks the privacy cost of the current location, but also increases the privacy risk of other locations. Therefore, even if a single location satisfies the ε-differential privacy requirement, it does not ensure that the entire trajectory satisfies ε-differential privacy [5] .…”

Section: Introductionmentioning

confidence: 99%

Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction

2024

AJCIS

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Aiming at the problems of user trajectory privacy budget and personalized demand of existing trajectory differential privacy protection technology, a personalized differential privacy protection mechanism based on sliding window and prediction perturbation is proposed. First, based on the road network topology, the sensitive road sections are classified into levels, and the allocation of personalized privacy budget is achieved by customizing the sensitivity of semantic locations. Then, recurrent neural networks and exponential perturbation methods are utilized to predict the perturbed locations that satisfy the differential privacy and temporal correlation requirements, and service similarity is introduced to detect location availability; If successful, the predicted location is directly used instead of the location of the differential perturbation, which reduces the privacy overhead from successive queries and further improves the utilization of the privacy budget. Finally, a w sliding window based trajectory budget allocation mechanism is designed to dynamically adjust the degree of privacy protection for each position in the trajectory according to the privacy needs of the user of the position. Experiments on real datasets show that the method can better achieve the balance between privacy and utility of trajectory data and improve the usability of published data while strictly protecting privacy.

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Real-time and private spatio-temporal data aggregation with local differential privacy

Cited by 13 publications

References 8 publications

Support Personalized Weighted Local Differential Privacy Skyline Query

Support Personalized Weighted Local Differential Privacy Skyline Query

Hide me Behind the Noise: Local Differential Privacy for Indoor Location Privacy

Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction

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