Time Distortion Anonymization for the Publication of Mobility Data with High Utility

Primault, Vincent; Mokhtar, Sonia Ben; Lauradoux, Cédric; Brunie, Lionel

doi:10.1109/trustcom.2015.417

Cited by 28 publications

(48 citation statements)

References 24 publications

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“…[64] present Promesse, a scheme for anonymizing mobile datasets. It conceals the point of interest and mobility habits of users by distorting time.…”

Section: Privacy and Securitymentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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Wireless big data describes a wide range of massive data that is generated, collected and stored in wireless networks by wireless devices and users. While these data share some common properties with traditional big data, they have their own unique characteristics and provide numerous advantages for academic research and practical applications. This article reviews the recent advances and trends in the field of wireless big data. Due to space constraints, this survey is not intended to cover all aspects in this field, but to focus on the data aided transmission, data driven network optimization and novel applications. It is expected that the survey will help the readers to understand this exciting and emerging research field better. Moreover, open issues and promising future directions are also identified.

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“…[64] present Promesse, a scheme for anonymizing mobile datasets. It conceals the point of interest and mobility habits of users by distorting time.…”

Section: Privacy and Securitymentioning

confidence: 99%

Survey of wireless big data

Qian

Zhu

Zhang

2017

J. Commun. Inf. Netw.

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“…GEO-I has been successfully applied in an online context when a user is querying an LBS in real-time, and in an offline context when an entire dataset gathering the mobility traces of a set of users is protected to be released. PROMESSE [8] is another protection mechanism whose goal is to hide POIs from traces via speed smoothing. More precisely, instead of obfuscating locations, PROMESSE obfuscates the temporal dimension of traces.…”

Section: Related Workmentioning

confidence: 99%

“…This metric assesses the spatial imprecision between locations before and after the obfuscation process, as we first proposed in [8]. The spatial distortion introduced by an LPPM has a direct impact on the utility.…”

Section: B Spatial Distortionmentioning

confidence: 99%

“…In ALP, α takes values in [0, 500] (meters), compared to an α ∈ {100, 200, 300, 500} for the static baselines. Baselines allow use to explore different values regularly spaced, including 500 meters, the maximum α considered by ALP and 200 meters, that α that should be globally optimal, according to [8]. Similarly to GEO-I, we also set as objectives for the optimizer to minimise the POIs retrieval with the same setting, but we set to maximise the area coverage for the utility metric.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…To address the challenge of location privacy, many location privacy protection mechanisms (LPPMs) have been recently proposed [6]- [9]. Some of these LPPMs (e.g., [6]- [8]) can be used offline by a system designer to protect a dataset before releasing it while others can also be used online by an end-user (e.g., [7]- [9]) to obfuscate a data portion before sending it to a given LBS. However, in both cases the effectiveness of these solutions usually rely on the tuning of a set of configuration parameters (possibly with a large range of possible values), which is a difficult task for nonexpert users or system designers as these parameters have both an impact on the privacy offered to the users and on the utility of the protected data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Location Privacy with ALP

Primault

Boutet

Mokhtar

et al. 2016

2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS)

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With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing of their location information. To deal with this issue, several location privacy protection mechanisms (LPPMs) have been proposed in the past years. However, each of these mechanisms comes with different configuration parameters that have a direct impact both on the privacy guarantees offered to the users and on the resulting utility of the protected data. In this context, it can be difficult for non-expert system designers to choose the appropriate configuration to use. Moreover, these mechanisms are generally configured once for all, which results in the same configuration for every protected piece of information. However, not all users have the same behaviour, and even the behaviour of a single user is likely to change over time. To address this issue, we present in this paper ALP, a new framework enabling the dynamic configuration of LPPMs. ALP can be used in two scenarios: (1) offline, where ALP enables a system designer to choose and automatically tune the most appropriate LPPM for the protection of a given dataset; (2) online, where ALP enables the user of a crowd sensing application to protect consecutive batches of her geolocated data by automatically tuning an existing LPPM to fulfil a set of privacy and utility objectives. We evaluate ALP on both scenarios with two real-life mobility datasets and two state-of-the-art LPPMs. Our experiments show that the adaptive LPPM configurations found by ALP outperform both in terms of privacy and utility a set of static configurations manually fixed by a system designer.

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Dynamic Modeling of Location Privacy Protection Mechanisms

Cerf

Mokhtar

Bouchenak

et al. 2018

Distributed Applications and Interoperable Systems

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Mobile applications tend to ask for users' location in order to improve the service they provide. However, aside from increasing their service utility, they may also store these data, analyze them or share them with external parties. These privacy threats for users are a hot topic of research, leading to the development of so called Location Privacy Protection Mechanisms. LPPMs often are configurable algorithms that enable the tuning of the privacy protection they provide and thus the leveraging of the service utility. However, they usually do not provide ways to measure the achieved privacy in practice for all users of mobile devices, and even less clues on how a given configuration will impact privacy of the data given the specificities of everyone's mobility. Moreover, as most Location Based Services require the user position in real time, these measures and predictions should be achieved in real time. In this paper we present a metric to evaluate privacy of obfuscated data based on users' points of interest as well as a predictive model of the impact of a LPPM on these measure; both working in a real time fashion. The evaluation of the paper's contributions is done using the state of the art LPPM Geo-I on synthetic mobility data generated to be representative of real-life users' movements. Results highlight the relevance of the metric to capture privacy, the fitting of the model to experimental data, and the feasibility of the online mechanisms due to their low computing complexity.

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Time Distortion Anonymization for the Publication of Mobility Data with High Utility

Cited by 28 publications

References 24 publications

Survey of wireless big data

Survey of wireless big data

Adaptive Location Privacy with ALP

Dynamic Modeling of Location Privacy Protection Mechanisms

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