Show me how you move and I will tell you who you are

Gambs, Sébastien; Killijian, Marc-Olivier; Núñez-del-Prado, Miguel

doi:10.1145/1868470.1868479

Cited by 146 publications

(138 citation statements)

References 21 publications

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“…Numerous privacy breaches have been proposed to illustrate how to breach the user privacy to obtain trajectory data [18,[22][23][24]. In this section, we briefly review the inference and linking attacks, anonymization techniques, adversary, and background knowledge of trajectory data analysis.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This knowledge could be workplaces, home addresses, social networks, places of interest, mobility patterns, physical conditions, and political views. For instance, recent studies [18,25,26] show that the movement patterns can be predicted from visited places. In the study by Song et al [26], the authors analysed 50,000 users' anonymized trajectories obtained from a mobile phone company, and the results show that 93% of the data could be used for mobility prediction.…”

Section: Inference and Linking Attacksmentioning

confidence: 99%

“…Based on movement patterns, there are some mobility models such as semantic trajectories that can be used to link places with semantic information [18]. A user's mobility behavior can be easily obtained from the user's frequently visited routes [27].…”

Section: Inference and Linking Attacksmentioning

confidence: 99%

See 2 more Smart Citations

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Hasan

et al. 2018

IJGI

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How can training performance data (e.g., running or walking routes) be collected, measured, and published in a mobile program while preserving user privacy? This question is becoming important in the context of the growing use of reward-based location-based service (LBS) applications, which aim to promote employee training activities and to share such data with insurance companies in order to reduce the healthcare insurance costs of an organization. One of the main concerns of such applications is the privacy of user trajectories, because the applications normally collect user locations over time with identities. The leak of the identified trajectories often results in personal privacy breaches. For instance, a trajectory would expose user interest in places and behaviors in time by inference and linking attacks. This information can be used for spam advertisements or individual-based assaults. To the best of our knowledge, no existing studies can be directly applied to solve the problem while keeping data utility. In this paper, we identify the personal privacy problem in a reward-based LBS application and propose privacy architecture with a bounded perturbation technique to protect user's trajectory from the privacy breaches. Bounded perturbation uses global location set (GLS) to anonymize the trajectory data. In addition, the bounded perturbation will not generate any visiting points that are not possible to visit in real time. The experimental results on real-world datasets demonstrate that the proposed bounded perturbation can effectively anonymize location information while preserving data utility compared to the existing methods.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Inference and Linking Attacksmentioning

confidence: 99%

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An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Hasan

et al. 2018

IJGI

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“…In this way, user's exposed locations can be transformed into his exposed POIs where he queries LBSs [35]. Indeed, it has been demonstrated that inference of POIs leads to a sever privacy breach [36].…”

Section: Motivation and Basic Ideas Inmentioning

confidence: 99%

A Context‐Aware Location Differential Perturbation Scheme for Privacy‐Aware Users in Mobile Environment

Zhang

Huang

et al. 2018

Wireless Communications and Mobile Computing

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The proliferation of location-based services, representative services for the mobile networks, has posed a serious threat to users' privacy. In the literature, several privacy mechanisms have been proposed to preserve location privacy. Location obfuscation enforced using cloaking region is a widely used technique to achieve location privacy. However, it requires a trusted third-party (TTP) and cannot sufficiently resist various inference attacks based on background information and thus is vulnerable to location privacy breach. In this paper, we propose a context-aware location privacy-preserving solution with differential perturbations, which can enhance the user's location privacy without requiring a TTP. Our scheme utilizes the modified Hilbert curve to project every 2-d location of the user in the considered map to 1-d space and randomly generates the reasonable perturbation by adding Laplace noise via differential privacy. In order to solve the resource limitation of mobile devices, we use a quad-tree based scheme to transform and store the user context information as bit stream which achieves the high compression ratio and supports efficient retrieval. Security analysis shows that our proposed scheme can effectively preserve the location privacy. Experimental evaluation shows that our scheme retrieval accuracy is increased by an average of 15.4% compared with the scheme using standard Hilbert curve. Our scheme can provide strong privacy guarantees with a bounded accuracy loss while improving retrieval accuracy.

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“…Gambs et al [7], [8] proposed a Mobility Markov-chain Model (MMC) to incorporate the n previous visited locations and its extension coined as n-MMC to predict next possible [9] conducted another evaluation of n-MMC in the indoor context and the prediction rate is only up to 49% when n=2. Two main realistic context differences lead to n-MMC cannot reach the similar prediction accuracy in indoor context as the one in outdoor context.…”

Section: Related Workmentioning

confidence: 99%

Wi-Fi Based Indoor Next Location Prediction Using Mixed State-Weighted Markov-Chain Model

Huang¹,

Dahlmeier²,

Lin³

et al. 2014

IJMLC

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I. INTRODUCTIONNowadays, Wi-Fi enabled connectivity has become one of the standard functions for smart phones and tablets with the trend of increasing number of indoor Wi-Fi enabled venues. For example, shopping malls provide free Wi-Fi connection to attract more shoppers. These free Wi-Fi services can bring more traffic to increase the visit volume of the shopping malls. Wi-Fi is also a "must-have" for retail stores [1]. Retailers want to be the special preference of their customers, in order to do that, they desire to have a direct relationship with their customers and remember their preferences. For example, once the customers come to their shops, these customers can be identified via Wi-Fi signal which is captured by their mobiles or tablets, a popup will be displayed in their mobile devices to inform them about new products or promotions and provide recommendations based on their historical purchase record.In addition, in order to attract more customers into the shopping mall, these Wi-Fi services can also be used to help capturing indoor shoppers' location. Given a particular location, the smart phone can be represented by its Wireless LANs Address (MAC) uniquely and the received strength of the Wi-Fi signal indicator (RSSI) from different access points Manuscript received April 9, 2014; revised July 8, 2014 can be measured. By modeling the distribution of the RSSI in different locations, an Indoor Positioning System (IPS) as in [2], [3] can be built and trained with collected RSSI data to predict the device location from the signal strength. Although there are other options for indoor positioning, such as the indoor-messaging-system (IMES) in which positioning uses the indoor-GPS devices [4], however, because of the low-cost setup and the simple infrastructure needed, the Wi-Fi based IPS is more attractive to many retailers. Normally, the accuracy of these Wi-Fi Based IPS ranges from 3 to 10 meters [5]. If we keep recording the location of a MAC address for a period of time, these chronological location data can represent the movement or trajectory of a specified person.In this work, we aim to develop a model to predict the pedestrian's next location based on his historical movement which can be represented by his trajectory. There are many potential ways in which users can benefit from this application of the next location prediction. By being aware of the movement of the shoppers in advance, the retailers can quickly target shoppers by both interest and location, turning window-shopping into actual sales [6]. In order to maximize the effect of coupon promotion, delivering the coupons in specified regions and at a particular time, namely "user oriented coupon dispersion", can be carried out. Leveraging the application of next location prediction, organizers can rearrange the manpower with more efficiency and at a lower cost.This paper presents an improved model for the next location prediction and the remainder of this paper is organized as follows. First, we review relevant related works in Section II b...

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Show me how you move and I will tell you who you are

Cited by 146 publications

References 21 publications

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

A Context‐Aware Location Differential Perturbation Scheme for Privacy‐Aware Users in Mobile Environment

Wi-Fi Based Indoor Next Location Prediction Using Mixed State-Weighted Markov-Chain Model

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