scholarly journals Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Bennati ◽  
Aleksandra Kovacevic
Keyword(s):  
Service Providers ◽  
Flow Analysis ◽  
Location Based Services ◽  
Location Based Service ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Imperfect Knowledge ◽  
Spatio Temporal ◽  
Definition Of ◽  
Privacy Risks

AbstractMobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target’s home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

scholarly journals Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Author(s):  
Huandong Wang ◽  
Qiaohong Yu ◽  
Yu Liu ◽  
Depeng Jin ◽  
Yong Li
Keyword(s):  
Service Providers ◽  
Auxiliary Information ◽  
Background Knowledge ◽  
Computational Time ◽  
Knowledge Graph ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Completion Problem ◽  
Spatio Temporal

With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge" extracted from multiple sources in a cohesive manner. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

scholarly journals Provable Security for Outsourcing Database Operations

2010 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Sergei Evdokimov ◽  
Matthias Fischmann ◽  
Oliver Günther
Keyword(s):  
Service Providers ◽  
Security And Privacy ◽  
Data Sets ◽  
Database Outsourcing ◽  
Confidential Data ◽  
Encryption Schemes ◽  
Database Operations ◽  
Definition Of ◽  
Database Service ◽  
Privacy Risks

Database outsourcing has become popular in recent years, although it introduces substantial security and privacy risks. In many applications, users may not want to reveal their data even to a generally trusted database service provider. Several researchers have proposed encryption schemes, such as privacy homomorphisms, that allow service providers to process confidential data sets without learning too much about them. In this paper, the authors discuss serious flaws of these solutions. The authors then present a new definition of security for homomorphic database encryption schemes that avoids these flaws and show that it is difficult to build a privacy homomorphism that complies with this definition. As a practical compromise, the authors present a relaxed variant of the security definition and discuss arising security implications. They present a new method to construct encryption schemes for exact selects and prove that the resulting schemes satisfy this notion.

scholarly journals Identifying Human Mobility via Trajectory Embeddings

2017 ◽  
Author(s):  
Qiang Gao ◽  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Goce Trajcevski ◽  
Xucheng Luo ◽  
...  
Keyword(s):  
Human Mobility ◽  
Route Planning ◽  
Classification Problem ◽  
Personalized Recommendation ◽  
Mobility Patterns ◽  
Classification Problems ◽  
Trajectory Data ◽  
Trajectory Classification ◽  
Spatio Temporal ◽  
Real World Datasets

Understanding human trajectory patterns is an important task in many location based social networks (LBSNs) applications, such as personalized recommendation and preference-based route planning. Most of the existing methods classify a trajectory (or its segments) based on spatio-temporal values and activities, into some predefined categories, e.g., walking or jogging. We tackle a novel trajectory classification problem: we identify and link trajectories to users who generate them in the LBSNs, a problem called Trajectory-User Linking (TUL). Solving the TUL problem is not a trivial task because: (1) the number of the classes (i.e., users) is much larger than the number of motion patterns in the common trajectory classification problems; and (2) the location based trajectory data, especially the check-ins, are often extremely sparse. To address these challenges, a Recurrent Neural Networks (RNN) based semi-supervised learning model, called TULER (TUL via Embedding and RNN) is proposed, which exploits the spatio-temporal data to capture the underlying semantics of user mobility patterns. Experiments conducted on real-world datasets demonstrate that TULER achieves better accuracy than the existing methods.

scholarly journals Translocation-Based Algorithm for Publishing Trajectories with Personalized Privacy Requirements

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Shuai Wang ◽  
Chunyi Chen ◽  
Guijie Zhang
Keyword(s):  
Privacy Protection ◽  
Location Based Services ◽  
Data Sets ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Privacy Requirements ◽  
Protection Scheme

Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

scholarly journals A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

2021 ◽  
Author(s):  
Fengmei Jin ◽  
Wen Hua ◽  
Matteo Francia ◽  
Pingfu Chao ◽  
Maria Orlowska ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Traffic Management ◽  
Real Life ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Data Publishing ◽  
Sensitive Information ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
The Right

<div>Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.</div>

scholarly journals Tis-Bad: A Time Series-Based Deobfuscation Algorithm

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
A Labrador ◽  
P. Wightman ◽  
A Santander ◽  
D Jabba ◽  
M. Jimeno
Keyword(s):  
Time Series ◽  
Location Privacy ◽  
Service Providers ◽  
Moving Average ◽  
Point Of View ◽  
Location Based Services ◽  
Location Obfuscation ◽  
Definition Of ◽  
Weighted Moving Average

Location-Based Services (LBSs) have  brought along many benefitsto users and service providers in terms of improved quality of existing services and a better user  experience. At the same time, location privacy has become one of the most critical concerns from the users’ point of view. One of  the existing techniques to protect the users’ location is through Location Obfuscation, which consists of  altering the location of the user while still allowing the provider to provide the requested service. Due to the simplicity of some techniques  of  this  kind, they may not offer enough protection against deobfuscation attacks (try to infer the original information from the obfuscated one), but there have not been much work on performance evaluation of them. This work presents a formal definition of a deobfuscation technique for noise-based obfuscation algorithms called TIS-BAD (Time Series - Based Deobfuscation) which implements an exponentially weighted moving average over the obfuscated data to filter the induced noise. In the literature there have been very few efforts to present such formal deobfuscation techniques, being this is one of the main contributions of this work. We evaluate the TIS-BAD algorithm against the Rand and N-Rand obfuscation algorithms, including both location and time scrambling, for straight and non-straight routes. The results show that the TIS-BAD algorithm can filter from 47% to 60% of the induced noise by the obfuscation algorithms, reducing considerably the protection on the users’ location information.

scholarly journals Representing Urban Functions through Zone Embedding with Human Mobility Patterns

2018 ◽  
Author(s):  
Zijun Yao ◽  
Yanjie Fu ◽  
Bin Liu ◽  
Wangsu Hu ◽  
Hui Xiong
Keyword(s):  
New York ◽  
Large Scale ◽  
Arrival Time ◽  
Human Mobility ◽  
Location Based Service ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Traffic Capacity ◽  
Spatio Temporal ◽  
Urban Functions

Urban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people’s various life needs. Understanding zone functions helps to solve a variety of urban related problems, such as increasing traffic capacity and enhancing location-based service. Therefore, it is beneficial to investigate how to learn the representations of city zones in terms of urban functions, for better supporting urban analytic applications. To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories. Specifically, we extract human mobility patterns from taxi trajectories, and use the co-occurrence of origin-destination zones to learn zone embeddings. To utilize the spatio-temporal characteristics of human mobility patterns, we incorporate mobility direction, departure/arrival time, destination attraction, and travel distance into the modeling of zone embeddings. We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data.

scholarly journals Provable Security for Outsourcing Database Operations

Cyber Crime ◽  
2013 ◽  
pp. 1603-1619
Author(s):  
Sergei Evdokimov ◽  
Matthias Fischmann ◽  
Oliver Günther
Keyword(s):  
Service Providers ◽  
Security And Privacy ◽  
Data Sets ◽  
Database Outsourcing ◽  
Confidential Data ◽  
Encryption Schemes ◽  
Database Operations ◽  
Definition Of ◽  
Database Service ◽  
Privacy Risks

Database outsourcing has become popular in recent years, although it introduces substantial security and privacy risks. In many applications, users may not want to reveal their data even to a generally trusted database service provider. Several researchers have proposed encryption schemes, such as privacy homomorphisms, that allow service providers to process confidential data sets without learning too much about them. In this paper, the authors discuss serious flaws of these solutions. The authors then present a new definition of security for homomorphic database encryption schemes that avoids these flaws and show that it is difficult to build a privacy homomorphism that complies with this definition. As a practical compromise, the authors present a relaxed variant of the security definition and discuss arising security implications. They present a new method to construct encryption schemes for exact selects and prove that the resulting schemes satisfy this notion.

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