Privacy Challenges in Third-Party Location Services

Damiani, Maria Luisa; Cuijpers, C.M.K.C.

doi:10.1109/mdm.2013.67

Cited by 15 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These LPPMs have been well surveyed in Liu et al [Liu, Darabi, Banerjee et al (2007); Ahmadi and Bouallegue (2017); Chow and Mokbel (2011); Krumm (2009)]. Upon these works, it is necessary to measure how many privacy these LPPMs really offered to the location, and the location privacy metric were studies in Wernke et al [Wernke, Skvortsov, Dürr et al (2014); Wang, Zhang and Yu (2015); Damiani and Cuijpers (2013); Tippenhauer, Rasmussen, Pöpper et al (2009)]. For the LBS scenario, these researches focused on how to use the location safely.…”

Section: Regulatory Mechanismmentioning

confidence: 99%

Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

Wang¹,

Sun²,

Su³

et al. 2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS). In DLBS, the service provider (SP) will not only send the information according to the user's location, more significant, he also provides a service device which will be carried by the user. DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London. In this paper, we, for the first time, blow the whistle of the new location privacy challenges caused by DLBS, since the service device is enabled to perform the localization without the permission of the user. To conquer these threats, we design a service architecture along with a credit system between DLBS provider and the user. The credit system tie together the DLBS device usability with the curious behaviour upon user's location privacy, DLBS provider has to sacrifice their revenue in order to gain extra location information of their device. We make the simulation of our proposed scheme and the result convince its effectiveness.

show abstract

Section: Regulatory Mechanismmentioning

confidence: 99%

Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

Wang¹,

Sun²,

Su³

et al. 2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Traverse the privacy ontology tree and find that there is a child node FirstName, SecondName, and LastName for Name, and a child node Country, Province, City, Street, and Community for Address. For a specific example of this step the reader can refer to our previous work in [3]. We do not include a corresponding expression in Figure 9.…”

Section: Case Studymentioning

confidence: 99%

“…In the domain of information systems and software engineering, privacy protection means the capability to prevent an individual's information from being collected, disclosed, and stored by others [3,4]. In 2002, the World Wide Consortium (W3C) developed the Platform for Privacy Preferences (P3P), which provides a standard and machineunderstandable privacy policy which is compared with the user's privacy preferences when the user visits a web site.…”

Section: Introductionmentioning

confidence: 99%

Privacy Data Decomposition and Discretization Method for SaaS Services

Huang

Xiao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In cloud computing, user functional requirements are satisfied through service composition. However, due to the process of interaction and sharing among SaaS services, user privacy data tends to be illegally disclosed to the service participants. In this paper, we propose a privacy data decomposition and discretization method for SaaS services. First, according to logic between the data, we classify the privacy data into discrete privacy data and continuous privacy data. Next, in order to protect the user privacy information, continuous data chains are decomposed into discrete data chain, and discrete data chains are prevented from being synthesized into continuous data chains. Finally, we propose a protection framework for privacy data and demonstrate its correctness and feasibility with experiments.

show abstract

“…Another concern related to collecting user location information is that it could be used for other purposes such as to infer habits or mobility patterns. In [13], for instance, the authors studied some location-privacy concerns related to untrusted LBSs. Furthermore, the same authors proposed a method to minimize the risk associated with the disclosure of mobile node trajectories by selecting which locations are sent to a LBS.…”

Section: Related Workmentioning

confidence: 99%

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

García¹,

Gómez²,

Lopez‐Guerrero³

et al. 2017

Mobile Information Systems

View full text Add to dashboard Cite

Location privacy in wireless networks is nowadays a major concern. This is due to the fact that the mere fact of transmitting may allow a network to pinpoint a mobile node. We consider that a first step to protect a mobile node in this situation is to provide it with the means to quantify how accurately a network establishes its position. To achieve this end, we introduce the location-exposure algorithm (LEA), which runs on the mobile terminal only and whose operation consists of two steps. In the first step, LEA discovers the positions of nearby network nodes and uses this information to emulate how they estimate the position of the mobile node. In the second step, it quantifies the level of exposure by computing the distance between the position estimated in the first step and its true position. We refer to these steps as alocation-exposureproblem. We tested our proposal with simulations and testbed experiments. These results show the ability of LEA to reproduce the location of the mobile node, as seen by the network, and to quantify the level of exposure. This knowledge can help the mobile user decide which actions should be performed before transmitting.

show abstract

Privacy Challenges in Third-Party Location Services

Cited by 15 publications

References 4 publications

Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

Privacy Data Decomposition and Discretization Method for SaaS Services

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

Contact Info

Product

Resources

About