DP-WHERE: Differentially private modeling of human mobility

Mir, Darakhshan; Isaacman, Sibren; Cáceres, Ramón; Martonosi, Margaret; Wright, Rebecca N.

doi:10.1109/bigdata.2013.6691626

Cited by 97 publications

(73 citation statements)

References 19 publications

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“…For example, the trajectory data provided by CDR tracks individuals and preferences risking their privacy as it is possible to uniquely track 95% of peoples' trajectories by knowing only four spatio-temporal points [139]. Though various methods, e.g., obfuscation [140], k-anonymity [141], differentially private model [71], [142], information fusion and aggregation [143], have been proposed, yet privacy protection remains an open challenge.…”

Section: E Data Privacy and Anonymitymentioning

confidence: 99%

“…Gonzalez et al reported spatial and temporal regularity in daily human mobility which can be represented using simple regular patterns [32]. The same principle was used to estimate the daily mobility tier which can be used to find the O-D matrices for the transportation [71]. Schneider et al investigated a method by combining the daily mobility patterns, called motifs, of different large-scale data sources which can be extended to a range of UA [53].…”

Section: A Mobile Phone Network Data Analysismentioning

confidence: 99%

“…Two features identify Home and Office, one is the frequency of user contacting with a BS during the evening and daytime respectively, and the other is the duration of stay at a place. Some techniques have also been proposed in the literature to find these places [29], [70], [96], [71]. This information can be validated using the census data.…”

Section: A Mobile Phone Network Data Analysismentioning

confidence: 99%

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Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g. mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This article mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, a possible crowd event detection framework is discussed which fuses data from all the available pervasive technology sources. In addition, available open-access crowd datasets for urban event detection are provided along with relevant Application Programming Interfaces, and finally, some open challenges and promising research directions are outlined.Index Terms-Urban sensing, pervasive technology, crowd mobility and management, information fusion, decision support system, benchmark datasets.

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Section: E Data Privacy and Anonymitymentioning

confidence: 99%

Section: A Mobile Phone Network Data Analysismentioning

confidence: 99%

Section: A Mobile Phone Network Data Analysismentioning

confidence: 99%

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Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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“…The goal of these papers is to compute differentially private histograms independently for each attribute (or jointly for a small number of attributes) and then try to generate a joint histogram for all attributes from the partial histograms. This was done for a data set of commuting patterns in [39] and for an arbitrary data set in [40]. In particular, [40] first tried to build a dependency hierarchy between attributes.…”

Section: Related Work On Differentially Private Data Publishingmentioning

confidence: 99%

“…Thus, in order to avoid obtaining too many sparsely populated bins, the number of bins per attribute must be significantly reduced (with the subsequent accuracy loss). An interesting approach to deal with multidimensional data is proposed in [39,40]. The goal of these papers is to compute differentially private histograms independently for each attribute (or jointly for a small number of attributes) and then try to generate a joint histogram for all attributes from the partial histograms.…”

Section: Related Work On Differentially Private Data Publishingmentioning

confidence: 99%

Utility-preserving differentially private data releases via individual ranking microaggregation

2016

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Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.

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CGAM: A community and geography aware mobility model

Mourchid

Kobbane

Othman

et al. 2017

Int J Communication

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SummaryThe advances of localization-enabled technologies have led to huge volumes of large-scale human mobility data collected from Call Data Records (CDR), Global Positioning System (GPS) tracking systems, and Location Based Social networks (LBSN). These location data that encompass mobility patterns could generate an important value for building accurate and realistic mobility models and hence important value for fields of application including context-aware advertising, city-wide sensing applications, urban planning, and more. In this paper, we investigate the underlying spatio-temporal and structural properties for human mobility patterns, and propose the Community and Geography Aware Mobility (CGAM) model, which characterizes user mobility knowledge through several properties such as home location distribution, community members' distribution, and radius of gyration. We validate the CGAM synthetic traces against real-world GPS traces and against the traces generated by the baseline mobility model SMOOTH and assess that CGAM is accurate in predicting the performance of flooding-based and community-based routing protocols. KEYWORDShuman mobility, mobility model, user patterns | INTRODUCTIONCharacterizing human mobility for different (daily or longtime) scales has received substantial attention, with the goal of designing mobility models that can express accurately several fundamental properties revealed in real traces of dynamic networks. The analysis, validation, and prediction of the performance of these mobility models could be fundamental for many fields of application ranging from context-aware advertising to epidemics prevention.Mobility models are classified into synthetic and trace-based mobility models. 1 The synthetic traces generated by a synthetic mobility model are far to be realistic, while those generated by a trace-based mobility model are expected to reproduce the statistical features present in human mobility. Therefore, the research community is more interested in the development and the use of mobility models that are based on real traces collected from several indoor and outdoor sites. 2 Moreover, many studies reported the discovery of fundamental statistical properties of human mobility such as flight length, flight duration, pause-times, and inter-contact times, extracted from real traces collected from bank notes tracking, Global Positioning System (GPS), CDR, Bluetooth encounters, and Location Based Social networks (LBSN) traces. 2 In this paper, we present a new mobility model, called Community and Geography Aware Mobility (CGAM) that is able to reproduce statistical properties of human mobility, to express geography, individual and community behavior features and thus simulating straightforwardly real-life scenarios. The CGAM model is proposed to combine the

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DP-WHERE: Differentially private modeling of human mobility

Cited by 97 publications

References 19 publications

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Utility-preserving differentially private data releases via individual ranking microaggregation

CGAM: A community and geography aware mobility model

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