Trajectory Privacy Preservation Research Articles

Trajectory privacy preservation model based on LSTM-DCGAN

Rapid scientific and technological development has brought many innovations to electronic devices, which has greatly improved our daily lives. Nowadays, many apps require the permission to access user location information, causing the concern on user privacy and making it an important task to protect user trajectory information. This paper proposes a novel model called LSTM-DCGAN by integrating LSTM (Long Short-Term Memory Network) with DCGAN (Deep Convolution Generative Adversarial Network). LSTM-DCGAN takes the advantages of LSTM to remember attributes in the trajectory data and the generator and the discriminator in DCGAN to generate and discriminate the trajectories. The proposed model is trained using real user trajectory data and the experimental results are validated from the perspectives of both effectiveness and practicality. Results show that the proposed LSTM-DCGAN model outperforms similar methods in generating synthesized trajectories that are similar to real trajectories in terms of the temporal and the spatial characteristics. In addition, various influencing factors are evaluated to investigate ways of further improving and optimizing the model. Overall, the proposed LSTM-DCGAN model can achieve the balance between the effectiveness of privacy protection and the practicality of user trajectory data and can thus be applied to safeguarding user trajectory information.

Achieving Privacy-Preserving Trajectory Query in Geospatial Information Systems With Outsourced Cloud

Achieving Privacy-Preserving Trajectory Query in Geospatial Information Systems With Outsourced Cloud

SoK: Can Trajectory Generation Combine Privacy and Utility?

While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research.

CTPP: A Conditional Trajectory Privacy Preservation Scheme Using Blockchain in V2X

CTPP: A Conditional Trajectory Privacy Preservation Scheme Using Blockchain in V2X

A Trajectory Privacy-Preserving Scheme Based on Transition Matrix and Caching for IIoT

A Trajectory Privacy-Preserving Scheme Based on Transition Matrix and Caching for IIoT

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities.

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k- 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k-1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.

CATS: Conditional Adversarial Trajectory Synthesis for privacy-preserving trajectory data publication using deep learning approaches

The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.

A privacy-preserving trajectory data synthesis framework based on differential privacy

Trajectory data contains spatio-temporal information about a large number of moving objects, and analysis of this data can yield a lot of valuable information to support a variety of application requirements related to moving objects. However, publishing unprotected trajectory data carries the risk of revealing personal privacy. In the existing approaches, the utility of published trajectory data is sacrificed for privacy protection. In this paper, we propose a differential privacy mechanism DP-Std for data publication in synthetic trajectory databases. first, the DP-Std mechanism constructs a density- and distance-aware adaptive grid structure for discrete geographic regions, and adds noise in the process to satisfy differential privacy. Second, DP-Std extracts feature distributions to ensure the utility of synthetic trajectories. Third, DP-Std constructs a mobility model that samples intermediate location points. And then the trajectory dataset is synthesized by differential privacy distribution and mobility model. Finally, we conduct experiments on two datasets to evaluate our framework. The results show that DP-Std outperforms other feature-based trajectory synthesis methods in terms of data utility and achieves a trade-off between privacy and utility with strict privacy protection.

Trajectory personalization privacy preservation method based on multi-sensitivity attribute generalization and local suppression

Fast-developing mobile location-aware services generate an enormous volume of trajectory data while adding value to people’s lives. However, trajectory data contains not only location information, but also sensitive personal information. If the original trajectory data is published directly, it could result in serious privacy leaks. Most of the existing privacy-preserving trajectory publishing methods only protect the location information or set the same privacy preservation levels for all moving objects. To meet the users’ personalized privacy requirements and ensure the utility of trajectory location and sensitive information, we propose a trajectory personalized privacy preservation method based on multi-sensitivity attribute generalization and local suppression. First, we set different security levels for each trajectory by calculating the correlation between sensitive attributes to establish a sensitive attribute classification tree. Second, we generalized sensitive attributes based on privacy preservation levels for each trajectory, the trajectory data still at risk of privacy leakage after generalization was locally suppressed. Finally, an anonymized trajectory dataset was generated. Experimental results on real datasets demonstrated that our method could improve data availability while preserving privacy.

TCPP: Achieving Privacy-Preserving Trajectory Correlation With Differential Privacy

TCPP: Achieving Privacy-Preserving Trajectory Correlation With Differential Privacy

ATPS: An AI Based Trust-Aware and Privacy-Preserving System for Vehicle Managements in Sustainable VANETs

Vehicular Ad hoc Networks (VANETs), as the integration of mobile vehicle and intelligent technology, has raised plenty of attentions. In VANETs, mobile smart vehicles can timely get information from surrounding environment, which brings great convenience for society. However, this highlights the need to improve data quality while protecting privacy for vehicular data provider. Thus, in this article, we propose an AI-based Trust-aware and Privacy-preserving System (ATPS) to preserve privacy for vehicular data providers while improving quality of data collections in VANETs. Our proposed ATPS system mainly consists of two schemes: 1) a Partial Ordering based Trust Management (POTM) scheme and 2) a Trajectory Privacy Preserving (TPP) scheme jointly designed by Wasserstein Generative Adversarial Networks (WGAN) and differential privacy. The POTM scheme uses partial ordering relationship to accurately evaluate and manage trusts for vehicular data providers with the assistance of fully trusted drones, and selects the vehicles with top ranks to collect data. Then, TPP scheme skillfully incorporates WGAN and differential privacy to preserve trajectory privacy for vehicular data providers in VANETs, which also guarantees data availability by adding carefully designed noise to the original trajectory. Compared to existing scheme, extensive experiments conducted on the real-world datasets demonstrates efficiency of our ATPS in terms of improving the data quality by 45.76% to 52.57%, reducing the malicious vehicle participants by 15.48% to 16.95%, preserving privacy of vehicles, and guaranteeing data availability.

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles.

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.

SEAT: Secure and Energy Efficient Anonymous Authentication With Trajectory Privacy-Preserving Scheme for Marine Traffic Management

An automatic identification system (AIS) provides significant improvements in traffic monitoring, collision avoidance, and ship assistance. A unique Maritime Mobile Service Identity (MMSI) is used by the AIS transceiver to identify the specific ship. However, an attacker can easily forge and tamper with unique MMSI. Also, the AIS transceiver sends the traffic information such as latitude, longitude, speed, MMSI, ship type, dimension, navigation status, etc. to the other ships and the base station continuously. This continuous communication of a ship with the base station or other ships may lead to the serious security threat of ship trajectory privacy. To overcome the above-discussed security threats, anonymous authentication with a trajectory privacy-preserving scheme is proposed. The security and performance analysis section ensures that the proposed scheme provides the necessary security features by consuming less computational and communication energy comparing to that of other competing systems.

Utility-aware and Privacy-preserving Trajectory Synthesis Model that Resists Social Relationship Privacy Attacks

For academic research and business intelligence, trajectory data has been widely collected and analyzed. Releasing trajectory data to a third party may lead to serious privacy leakage, which has spawned considerable researches on trajectory privacy protection technology. However, existing work suffers from several shortcomings. They either focus on point-based location privacy, ignoring the spatio-temporal correlations among locations within a trajectory, or they protect the privacy of each user separately without considering privacy leakage of the social relationship between trajectories of different users. Besides, they fail to balance privacy protection and data utility. Motivated by these limitations, in this article, we propose S 3 T -Trajectory, which is a utility-aware and privacy-preserving trajectory synthesis model that Resists social relationship privacy attacks. Specifically, we first develop a time-dependent Markov chain based on an adaptive spatio-temporal discrete grid to efficiently and accurately capture human mobility behavior. Then, we propose three mobility feature metrics from spatio-temporal, semantic, and social dimensions. On the basis of the metrics, we construct a bi-level optimization problem to accomplish the utility-aware and privacy-preserving trajectory synthesizing. The upper-level objective guarantees data utility and the lower-level optimization problems (or upper-level constraints) provides two-layer privacy protection for S 3 T -Trajectory, i.e., resisting location inference attacks and social relationship privacy attacks. We conduct extensive experiments on large-scale real-world datasets loc-Gowalla and loc-Brightkite. The experimental results demonstrate the effectiveness and robustness of S 3 T Trajectory. Compared with the baseline models, S 3 T Trajectory achieves between 7.8% and 23.8% performance improvement in resisting social relationship privacy attacks and achieves at least 5.19% improvement regarding data utility.

Trajectory Privacy Preserving for Continuous LBSs in VANET

Location-based services (LBSs) support various applications in vehicular ad hoc network (VANET). However, location/trajectory privacy becomes a serious concern for LBSs. Existing location/trajectory privacy-preserving schemes rarely take the attack model of adversaries into consideration, and the cost for achieving privacy has not been carefully studied. To deal with these problems, this study proposes a collaborative trajectory obfuscation scheme based on analyzing the attack model and designs a privacy-preserving efficiency metric that balances the achieved privacy and the cost. Through simulation, the effects of the density of vehicles using the same LBSs on the performance of our design and an existing scheme are investigated. The performance comparison results validate the effectiveness and efficiency of our scheme.

Exchanging registered users' submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks.

In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can be easily obtained by adversaries who SPs has compromised with. Even worse, existing techniques, such as cryptography and generalization, etc., are infeasible due to the necessity of public publication of reviews and the facticity of reviews. Inspired by pseudonym techniques, we propose an approach to exchanging reviews before users submit reviews to SPs. In our approach, we introduce two attacks, namely review-based location correlation attack (RLCA) and semantic-based long-term statistical attack (SLSA). RLCA can be exploited to link the real user by reconstructing the trajectory, and SLSA can be launched to establish a connection between locations and users through the difference of semantic frequency. To resist RLCA, we design a method named User Selection to Resist RLCA (USR-RLCA) to exchange reviews. We propose a metric to measure the correlation between a user and a trajectory. Based on the metric, USR-RLCA can select reviews resisting RLCA to exchange by suppressing the number of locations on each reconstructed trajectory below the correlation. However, USR-RLCA fails to resist SLSA because of ignoring the essential semantics. Hence, we design an enhanced USR-RLCA named User Selection to Resist SLSA (USR-SLSA). We first propose a metric to measure the indistinguishability of locations concerning the difference of semantic frequency in a long term. Then, USR-SLSA can select reviews resisting SLSA to exchange by allowing two reviews whose indistinguishability is below the probability difference after the exchange to be exchanged. Evaluation results verify the effectiveness of our approach in terms of privacy and utility.

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks

Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis, we prove that DP-LTOD scheme can guarantee ϵ-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

PPTPF: Privacy-Preserving Trajectory Publication Framework for CDR Mobile Trajectories

As mobile phone technology evolves quickly, people could use mobile phones to conduct business, watch entertainment shows, order food, and many more. These location-based services (LBS) require users’ mobility data (trajectories) in order to provide many useful services. Latent patterns and behavior that are hidden in trajectory data should be extracted and analyzed to improve location-based services including routing, recommendation, urban planning, traffic control, etc. While LBSs offer relevant information to mobile users based on their locations, revealing such areas can pose user privacy violation problems. An efficient privacy preservation algorithm for trajectory data must have two characteristics: utility and privacy, i.e., the anonymized trajectories must have sufficient utility for the LBSs to carry out their services, and privacy must be intact without any compromise. Literature on this topic shows many methods catering to trajectories based on GPS data. In this paper, we propose a privacy preserving method for trajectory data based on Call Detail Record (CDR) information. This is useful as a vast number of people, particularly in underdeveloped and developing places, either do not have GPS-enabled phones or do not use them. We propose a novel framework called Privacy-Preserving Trajectory Publication Framework for CDR (PPTPF) for moving object trajectories to address these concerns. Salient features of PPTPF include: (a) a novel stay-region based anonymization technique that caters to important locations of a user; (b) it is based on Spark, thus it can process and anonymize a significant volume of trajectory data successfully and efficiently without affecting LBSs operations; (c) it is a component-based architecture where each component can be easily extended and modified by different parties.

Privacy-Preserving Trajectory Data Publishing by Dynamic Anonymization with Bounded Distortion

Publication of trajectory data that contain rich information of vehicles in the dimensions of time and space (location) enables online monitoring and supervision of vehicles in motion and offline traffic analysis for various management tasks. However, it also provides security holes for privacy breaches as exposing individual’s privacy information to public may results in attacks threatening individual’s safety. Therefore, increased attention has been made recently on the privacy protection of trajectory data publishing. However, existing methods, such as generalization via anonymization and suppression via randomization, achieve protection by modifying the original trajectory to form a publishable trajectory, which results in significant data distortion and hence a low data utility. In this work, we propose a trajectory privacy-preserving method called dynamic anonymization with bounded distortion. In our method, individual trajectories in the original trajectory set are mixed in a localized manner to form synthetic trajectory data set with a bounded distortion for publishing, which can protect the privacy of location information associated with individuals in the trajectory data set and ensure a guaranteed utility of the published data both individually and collectively. Through experiments conducted on real trajectory data of Guangzhou City Taxi statistics, we evaluate the performance of our proposed method and compare it with the existing mainstream methods in terms of privacy preservation against attacks and trajectory data utilization. The results show that our proposed method achieves better performance on data utilization than the existing methods using globally static anonymization, without trading off the data security against attacks.