Check-ins and Photos: Spatiotemporal Correlation-Based Location Inference Attack and Defense in Location-Based Social Networks

Shahid, Abdur R.; Pissinou, Niki; Iyengar, S. Sitharama; Makki, Kia

doi:10.1109/trustcom/bigdatase.2018.00280

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…Trajectory privacy protection methods considering correlations within a single trajectory. e spatiotemporal correlation contained in the trajectory data easily leads to the privacy leakage problem of the users [31]. Many researchers have proposed trajectory privacy-preserving methods considering the temporal and spatial correlation within a trajectory.…”

Section: Related Workmentioning

confidence: 99%

“…Literature Limitations Methods without considering the correlation Suppression [4] and bounded perturbation [22] Kanonymity and its derivation [3,23,24]differential privacy [25][26][27][28][29][30] Face combination attacks and background knowledge attacks, and do not consider the privacy leakage caused by trajectory correlations Methods considering correlations within a single trajectory [6][7][8]31] Do not consider the privacy leakage caused by trajectory correlations…”

Section: Categorymentioning

confidence: 99%

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Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

Zhu

Xie

2022

Security and Communication Networks

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With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the problem of protecting trajectory correlations into reducing the trajectory similarities of users and preserving the utility of the perturbed trajectories. Based on the insight, we model a multi-objective optimization problem and solve the problem with the particle swarm optimization algorithm modified to satisfy differential privacy. Then we generate synthetic trajectories and the correlations between trajectories are reduced. We conduct extensive experiments on three real trajectory datasets. The experimental results show that RDPT achieves almost equivalent data utility to and better privacy than the existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Categorymentioning

confidence: 99%

Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

Zhu

Xie

2022

Security and Communication Networks

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“…In location-based social networks, user interaction is mainly through check-in and photo sharing. The study [62] proposed an inference model based on the spatial distribution of historical check-in and photos. By analyzing multiple events, including check-in and photos, the user's location can be inferred with high accuracy.…”

Section: Social Behavior-based Approachesmentioning

confidence: 99%

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Piao

Cui

2021

IEEE Access

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With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users' personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users' privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

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“…A major improvement of vanilla k-anonymity is the dummy approach in which k − 1 number of dummy locations are directed with the real one to a service provider without relying on a TTP. 4,[18][19][20][21][22][23][24][25][26] However, the majority of the existing methods generate the dummies at random. 4 19 DLS, enhanced-DLS 20 methods improve this limitation by considering probability of submitting queries from locations.…”

Section: Related Workmentioning

confidence: 99%

“…However, reliance on a TTP to get a real user's information is a serious limitation of these approaches. A major improvement of vanilla k ‐anonymity is the dummy approach in which k − 1 number of dummy locations are directed with the real one to a service provider without relying on a TTP 4,18‐26 . However, the majority of the existing methods generate the dummies at random 4 .…”

Section: Related Workmentioning

confidence: 99%

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

Shahid

Pissinou

Iyengar

et al. 2020

Int J Communication

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The ubiquitous use of location-based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long-term observation of privacy-preserving queries. We show that existing privacy-preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy-preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimum k-locations, l-diversity, and privacy area preservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long-term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real-world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state-of-the-art methods.

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Check-ins and Photos: Spatiotemporal Correlation-Based Location Inference Attack and Defense in Location-Based Social Networks

Cited by 4 publications

References 16 publications

Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Delay‐aware privacy‐preserving location‐based services under spatiotemporal constraints

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