Dummy Trajectories Research Articles

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.

Privacy-Preserving Method for Trajectory Data Publication Based on Local Preferential Anonymity

With the rapid development of mobile positioning technologies, location-based services (LBSs) have become more widely used. The amount of user location information collected and applied has increased, and if these datasets are directly released, attackers may infer other unknown locations through partial background knowledge in their possession. To solve this problem, a privacy-preserving method for trajectory data publication based on local preferential anonymity (LPA) is proposed. First, the method considers suppression, splitting, and dummy trajectory adding as candidate techniques. Second, a local preferential (LP) function based on the analysis of location loss and anonymity gain is designed to effectively select an anonymity technique for each anonymous operation. Theoretical analysis and experimental results show that the proposed method can effectively protect the privacy of trajectory data and improve the utility of anonymous datasets.

Dummy trajectory generation scheme based on generative adversarial networks

Dummy trajectory generation scheme based on generative adversarial networks

Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing.

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices, trajectory data is continuously growing. While large-scale trajectories provide opportunities for various applications, the locations in trajectories pose a threat to individual privacy. Recently, there has been an interesting debate on the reidentifiability of individuals in the Science magazine. The main finding of Sánchez et al. is exactly opposite to that of De Montjoye et al., which raises the first question: “what is the true situation of the privacy preservation for trajectories in terms of reidentification?” Furthermore, it is known that anonymization typically causes a decline of data utility, and anonymization mechanisms need to consider the trade-off between privacy and utility. This raises the second question: “what is the true situation of the utility of anonymized trajectories?” To answer these two questions, we conduct a systematic experimental study, using three real-life trajectory datasets, five existing anonymization mechanisms (i.e., identifier anonymization, grid-based anonymization, dummy trajectories, k-anonymity and ε-differential privacy), and two practical applications (i.e., travel time estimation and window range queries). Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories, and essentially close the debate between De Montjoye et al. and Sánchez et al. To the best of our knowledge, this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11390-022-2409-x.

A Trajectory Privacy Protect Method Based on Location Pair Reorganization

With the rapid development of mobile Internet and communication technology, location-based services (LBS) are widely used in our daily life. The server stores a large amount of user location data, and these location data constitute user trajectories. If trajectory information on the server is leaked, it will seriously endanger users’ privacy. Trajectory k -anonymity technology is one of the most important methods to protect the privacy of user trajectory. However, current trajectory k -anonymity methods have less discussion on the semantic of stop point when selecting dummy trajectory, which leads to the fact that attacker can still exclude the dummy trajectory from the k -anonymity set and infer the real trajectory by combining background knowledge with the semantic information of stop points. To address this problem, this paper decomposes the real trajectory into location pairs set; the set consists of start-end points and stop points. According to the similarity of location pairs, the similar location pairs in history trajectory set are used to generate dummy trajectory: firstly, extracting the start-end points and stop points from real trajectory and assigning semantic to them. Then, based on the semantic, temporal, and geographical attributes, eligible location pairs are selected from history trajectory set to construct equivalence class. Finally, according to the location pairs in equivalence class, k − 1 dummy trajectories are generated to form a k -anonymity set. We evaluate our method thoroughly with real dataset. The results show that our method achieve an effective data availability and higher privacy protection than other methods.

DLP: Achieve Customizable Location Privacy With Deceptive Dummy Techniques in LBS Applications

As a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. Aiming at the challenge, in this article, we first build up a detect module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy preserving scheme, named dummy location provider (DLP), which comprises three algorithms, namely, Spread, Shift, and Switch. Specifically, Spread and Shift are in charge of generating deceptive dummies and trajectories. And with Switch, users’ real locations are replaced with dummy trajectories before being submitted to LBS applications. As a result, users can not only prevent applications from accessing location data arbitrarily, but also avoid being questioned by applications in terms of honesty. Furthermore, to guarantee necessary functions of LBS, DLP offers customizable privacy-preserving strategies for users, which can achieve flexible location data usage control. Finally, our DLP can also attain achievable and effortless deployment over smart devices. Detailed security analysis indicates that DLP resists inference attacks even facing skeptical applications. In addition, for performance evaluation, a DLP application (DLPA) is developed on the Android platform and tested in the real environment, and the extensive experimental results demonstrate that the DLPA is indeed effective and high efficiency in practice.

Evidence of predictive selective attention in fiddler crabs during escape in the natural environment.

Selective attention is of fundamental relevance to animals for performing a diversity of tasks such as mating, feeding, predation and avoiding predators. Within natural environments, prey animals are often exposed to multiple, simultaneous threats, which significantly complicates the decision-making process. However, selective attention is rarely studied in complex, natural environments or in the context of escape responses. We therefore asked how relatively simple animals integrate the information from multiple, concurrent threatening events. Do they identify and respond only to what they perceive as the most dangerous threat, or do they respond to multiple stimuli at the same time? Do simultaneous threats evoke an earlier or stronger response than single threats? We investigated these questions by conducting field experiments and compared escape responses of the fiddler crab Gelasimus dampieri when faced with either a single or two simultaneously approaching dummy predators. We used the dummies' approach trajectories to manipulate the threat level; a directly approaching dummy indicated higher risk while a tangentially approaching dummy that passed the crabs at a distance represented a lower risk. The crabs responded later, but on average more often, when approached more directly. However, when confronted with the two dummies simultaneously, the crabs responded as if approached only by the directly approaching dummy. This suggests that the crabs are able to predict how close the dummy's trajectory is to a collision course and selectively suppress their normally earlier response to the less dangerous dummy. We thus provide evidence of predictive selective attention within a natural environment.

A Semantic-Based Approach for Privacy-Preserving in Trajectory Publishing

The increasing abundance of data in the trajectories of personal movement opens up new opportunities for analyzing and mining human mobility. But the inherent personal information in the data raises the privacy concern. In this paper, the trajectories are not simply considered as a sequence of the coordinates in Euclidean space, they combine the semantics-aware information with the background knowledge of underlying map for the location points. A novel approach is then proposed to conceal the actually visited sensitive places. To this end, a cloaking region for each personalized sensitive place is built before a reasonable dummy trajectory in the cloaking region is constructed. The performance of our approach is evaluated based on a real-world trajectories dataset. And the results show that the proposed approach outperforms the existing privacy-preserving approaches.

MoveWithMe: Location Privacy Preservation for Smartphone Users

With the prevalence of smartphones, mobile websites have been more and more popular. However, many mobile websites collect the location information which greatly increases users’ risks of being tracked unexpectedly. The current location access control setting is not sufficient since it cannot prevent the service providers which have been granted location-access permissions from tracking the users. In this paper, we propose a novel location privacy preservation mobile app, called MoveWithMe, which automatically generates decoy queries to hide the real users’ locations and intentions when they are using location-based mobile services. Unlike the existing works on dummy trajectories which may be easily discovered by attackers through data analysis, the uniqueness of the MoveWithMe app is that our generated decoys closely behave like real humans. Each decoy in our system has its own moving patterns, daily schedules, and social behaviors, which ensures its movements to be semantically different from the real user’s trace and satisfying geographic constraints. Thus, our decoys can hardly be distinguished even by advanced data mining techniques. Another advantage of the MoveWithMe app is that it guarantees the same level of user experience without affecting the response time or introducing extra control burdens. Decoys move independently in the back end and automatically submit queries to the same service provider whenever the user does so. Our proposed MoveWithMe app has both iOS and Android versions and has been tested on different brands of smartphones against various location-based services, such as Yelp and TripAdvisor. Experimental results demonstrate its practicality, effectiveness, and efficiency.

Detection of Dummy Trajectories Using Convolutional Neural Networks

Nowadays, privacy in trajectory is an important issue in the coming big data era. In order to provide better protection for trajectory privacy, a number of solutions have been proposed in the literature, and the dummy trajectory method has attracted great interests in both academia and industry recently due to the following advantages: (1) neither a third-party server nor other parties’ cooperation is necessary; (2) location-based services are not influenced; and (3) its algorithm is relatively simple and efficient. However, most of trajectory privacy generations usually consider the geometric shape of the trajectory; meanwhile the real human mobility feature is usually neglected. In fact, the real trajectory is not the product of random probability. In this paper, convolutional neural network (CNN) is used as the learning machine to train with lots of the real trajectory and the generated dummy trajectory sets. Then, the trained classifier is used to distinguish the dummy from the real trajectory. Experiments demonstrate that the method using CNN is very efficient, and more than 90% of dummy trajectories can be detected. Moreover, the real trajectory erroneous judgment rate is below 10% for most of real trajectories.

KNN search-based trajectory cloaking against the Cell-ID tracking in cellular network

The widely used smartphone with powerful positioning capability makes it easy for a user to find his precise physical location. However, this may reveal a user’s geo-location information, making the real-time tracking of the user possible. For example, on the basis of a sequence of numbers (i.e., Cell-IDs) received in the Cell-ID positioning, an entity can gain access to a person’s movement routes without his consent. We argue that if the trajectory of a person is traced, then all his visits may be exposed. Therefore, trajectory cloaking against the mobile positioning is urgently necessary. In this paper, we propose a dummy base station replacement (DBSR) algorithm. It mainly uses the idea of dummy trajectory anonymity and is achieved by replacing the true Cell-ID provided by the network with a fake but nearby Cell-ID. We also implement our DBSR algorithm on an Android-based smartphone to evaluate its performance. Experimental results show that the DBSR algorithm can efficiently tackle the privacy breach caused by the single-base-station positioning in cellular network.

Trajectory Protection Schemes Based on a Gravity Mobility Model in IoT

With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

RcDT: Privacy Preservation Based on R-Constrained Dummy Trajectory in Mobile Social Networks

The boom of mobile devices and location-based services (LBSs) greatly enriches the mobile social network (MSN) applications, which bring convenience to our daily life and, meanwhile, raise serious privacy concerns due to the potential disclosure risk of location privacy. Besides the single-location privacy, trajectory privacy is another important type for location privacy leakage. In this paper, focusing on the trajectory privacy preservation in MSNs, we propose a privacy preservation scheme based on the radius-constrained dummy trajectory (RcDT) in MSNs. Particularly, by constraining the generated circular range with radius R for the location where a user sends LBS requests, we present the radius-constrained dummy location (RcDL) algorithm to generate the dummy location set of the user's real location. Furthermore, based on the generated dummy locations, we put forward the RcDT algorithm to generate the dummy trajectory set that has higher similarity to the real trajectory comprehensively considering the constraints of both the single-location exposure risk and trajectory exposure risk. Thus, the user's trajectory privacy preservation in MSNs is enhanced since the possibility of identifying users' real trajectories and malicious attacks are reduced. The simulation results demonstrate that our RcDT scheme can have better performance and privacy degree than the existing methods.

Trajectory Rotation Privacy Protection Algorithm Based on <i>k</i> Anonymity

The frequent use of location query services in location-based services will come out a large amount of space-time data related to users. Attackers infer information of location or track based on these rich background knowledge. Therefore, aiming at the problem of trajectory privacy, the context adds instant traffic monitoring based on user behavior patterns, trajectory similarity and other background information. According to the idea of k anonymity, proposed a method combined with traffic condition to protect the trajectory privacy. First, the user randomly selects a time point of the real trajectory to rotate to generate dummy trajectory, and then repeat the above process on the real trajectory and dummy trajectory. Up to the generation of k − 1 dummy trajectory, and according to the actual road conditions and trajectory leakage probability, traversing dummy trajectory to adjust. Finally, it is further proved through experiments that the method will be more efficient and protect privacy well.

A Privacy Preserving Algorithm Based on R-constrained Dummy Trajectory in Mobile Social Network

Recently the research of location privacy preserving has become a hot spot. Location privacy preserving involves not only a single location privacy but also the trajectory privacy where the mobile users in different locations publish the consecutive query requests. In this paper, we consider the problem of trajectory privacy preserving in MSN. Particularly, we propose privacy preserving algorithms based on R-constraint dummy trajectory (RcDT). By constraining the generating range R of the dummy positions, the generated dummy positions are within a certain range near the real locations. Furthermore, the dummy trajectories which have higher similarity to the real trajectories are generated via constraining both the single location exposure risk and the trajectory exposure risk.

Dummy Generation Based on User-Movement Estimation for Location Privacy Protection

Location-based services (LBSs) have been becoming more common due to the prevalence of GPS-enabled devices. While LBSs bring many benefits for our daily lives, location information may reveal private information, rendering an important problem of protecting location privacy of users. To anonymize the locations of users, we focus on dummy-based approaches that generate dummies and their locations are sent along with the actual location of a user to an LBS provider. Although several existing studies developed dummy-based techniques, they assume unrealistic user mobility, e.g., users keep moving and do not stop or follow a pre-defined movement plan precisely. In this paper, we remove the unrealistic assumptions and require much easier input with respect to user movement, i.e., only a set of visiting points. Under the assumption, we propose a dummy generation method, estimation-based dummy trajectory generation (Edge). Based on the given visiting points, Edge estimates a user-movement plan and designs trajectories of dummies so that the adversaries cannot distinguish the user from dummies. We conduct extensive experiments using real map information, and the results show the efficiency and effectiveness of Edge.

TPP: Trajectory Privacy Preservation Against Tensor Voting Based Inference Attacks

The popularity of mobile devices with global positioning system (GPS) has boosted various wireless location-based services (LBSs). Certain honest-but-curious or even dishonest LBS servers may learn the users’ trajectories from location trace files, and the users’ privacy can be compromised. In this paper, we propose a quantitative approach to model trajectory inference attacks via tensor voting, which can be widely applied in computer vision and machine learning as a perceptual organization. To counter the tensor voting based attacks, we propose a novel trajectory privacy preservation TPP scheme, in which LBS users will intentionally generate dummy trajectories to obfuscate LBS servers. Meanwhile, the LBS users have the option to disclose their trajectories to trustworthy parties (e.g., users’ parents) by sending those parties a few more encrypted locations. Considering the power constraint of hand-held mobile devices, we mathematically formulate the trajectory privacy preservation problem into a mixed integer linear programming optimization problem and propose the algorithms for optimizing solutions. Through simulations and analysis, we show that the proposed scheme can effectively preserve LBS users’ trajectory privacy against tensor voting-based inference attacks with limited power consumption.

The framework and algorithm for preserving user trajectory while using location-based services in IoT-cloud systems

Internet of things (IoT) based location-based services (LBS) are playing an increasingly important role in our daily lives. However, since the LBS server may be hacked, malicious or not credible, there is a good chance that interacting with the LBS server may result in loss of privacy.As per journal instruction, author photo and biography are mandatory for this article. Please provide. Thus, protecting user privacy such as the privacy of user location and trajectory is an important issue to be addressed while using LBS. To address this problem, we first construct three kinds of attack models that may expose a user's trajectory or path while the user is sending continuous queries to a LBS server. Then we construct a novel LBS system model for preserving privacy, and propose the k-anonymity trajectory (KAT) algorithm which is suitable for both single query and continuous queries. Different from existing works, the KAT algorithm selects $$k-1$$k-1 dummy locations using the sliding window based k-anonymity mechanism when the user is making single query, and selects $$k-1$$k-1 dummy trajectories using the trajectory select mechanism for continuous queries. We evaluate the effectiveness of our proposed algorithm by conducting simulations for the single-query and continuous-query scenarios. The simulation results show that our proposed algorithm can protect privacy of users better than existing approaches, while incurring a lower time complexity than those approaches.

The influence of a flexible lumbar spine in far-side impact testing

Recent findings have shown the nearside impact dummies BioSID and EuroSID to be less applicable in far-side testing. In this paper, the lumbar spine of a BioSID dummy was replaced with a coil spring to enable a more extensive shearing, bending and elongation of the dummy spine. Using a sled test method developed to replicate a 65-km/h impact full-scale far-side test, the kinematics and injury measures of the modified dummy were compared to a Post Mortem Human Subject (PMHS) full-scale crash. These results showed that the dummy trajectory and acceleration measures reasonably predicted the PMHS movement although further validation is required. Also, a series of BioSID far-side sled tests was performed with and without the spine modification during various loading conditions. These results showed the spring to remarkably change the Head Injury Criteria (HIC) values, kinematics, and loads. In conclusion, the spring spine ‘when fitted to the BioSID test dummy,’ was found to be suitable to manifest injurious test circumstances in accordance with the PMHS test as well as real-life crashes, although more research is clearly needed.