Preventing velocity-based linkage attacks in location-aware applications

Ghinita, Gabriel; Damiani, Maria Luisa; Silvestri, Claudio; Bertino, Elisa

doi:10.1145/1653771.1653807

Cited by 95 publications

(85 citation statements)

References 12 publications

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“…A variety of approaches have been proposed to overcome privacy protection related challenges. Many depend on specific scenarios and basic privacy attributes such as the mobile user's identity, his current location, and time information [9]. For instance, a mobile user, who is at an unknown and unimportant location, may have no issue in sharing his personal data.…”

Section:  What If the Mobile User's Location Is Revealed?mentioning

confidence: 99%

“…They take advantage of the rare case in k-anonymity, where a sensitive value is indistinguishable and posted along a set of k-cluster values. Despite the dataset being k-anonymized, the sensitive value is revealed by any adversary [8], [9]. Additional homogeneity attacks include map utilization by reducing the area.…”

Section: ) Location Homogenity Attacksmentioning

confidence: 99%

“…The attacker effectively shrinks the specific region based on where he got consecutive query updates of a particular kanonymity set and corresponding actual query [9], [11].…”

Section: ) Background Knowledge Attackmentioning

confidence: 99%

See 2 more Smart Citations

AES-Route Server Model for Location based Services in Road Networks

Alrahhal¹,

Ashraf²,

Abesen³

et al. 2017

ijacsa

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Abstract-The now ubiquitous use of location based services (LBS), within the mobile computing domain, has enabled users to receive accurate points of interest (POI) to their geo-tagged queries. While location-based services provide rich content, they are not without risks; specifically, the use of LBS poses many serious challenges with respect to privacy protection. Additionally, the efficiency of spatial query processing, and the accuracy of said results, can be problematic when applied to road networks. Existing approaches provide different online route APIs to deliver the precise POI, but mobile user demand not only Accurate, Efficient and Secure (AES) results, but results that do not threaten their privacy. In this paper, we have addressed these challenges by proposing an AES-Route Server (RS) approach for LBS, which supports common spatial queries, including Range Queries and k-Nearest Neighbor Queries. We can secure the user location through the proposed AES-RS model because it provides the query results accurate and efficiently. The proposed model satisfies the primary goals including accuracy, efficiency and privacy for a location base system.

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Section:  What If the Mobile User's Location Is Revealed?mentioning

confidence: 99%

Section: ) Location Homogenity Attacksmentioning

confidence: 99%

See 1 more Smart Citation

AES-Route Server Model for Location based Services in Road Networks

Alrahhal¹,

Ashraf²,

Abesen³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…The problem of location privacy has been well studied in the context of location-based services (Rossi et al, 2015;Olumofin and Tysowski, 2010;Gruteser and Grunwald, 2003;Ghinita and Damiani, 2009;Damiani et al, 2010;Hwang et al, 2014;Ghinita and Damiani, 2009;Ghinita, 2013;Jin et al, 2010), but mainly with a focus on on-line, service-focused anonymity. In this paper, we consider off-line and data-focused anonymity, as in the context of data publishing.…”

Section: Relatad Workmentioning

confidence: 99%

The Trade-Off Between Privacy and Geographic Data Resolution. A Case of GPS Trajectories Combined With the Social Survey Results

Siła-Nowicka

Thakuriah

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Trajectory datasets are being generated in great volumes due to high levels of Global Positioning System (GPS) and Location-Based Services (LBS) use. Such data are increasingly being collected for a variety of academic, industrial and recreational reasons, sometimes together with other strands of personal data such as socio-demographic, social survey and other sensor data carried/worn by the person. In such cases, not only are movement data of a person available but also data on potentially a wide variety of other personal and household attributes. Making such person-level data available for analytics opens up the possibility of new directions in analysing, studying and understanding human behaviour, which is typically not possible with GPS trajectory datasets alone. At the same time, the GPS data should be released in a privacy-preserving way that takes into account the possibility of re-identification of individuals from quasi-identifiers available from other data strands. De-identification in these strands may be risked due to uniquely identifiable information on significant locations and other spatial behaviours and choices detected from GPS trajectories. Using a multimodal dataset that includes a GPS archive from 358 individuals, and by considering a number of alternative privacy-enhancing approaches,

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“…To reduce the computational complexity and improve the service quality, algorithms based on disturbance were proposed [7,11,12]. In this type, algorithms usually disturb the real location by other k-1 users with the help of a trusted third party (TTP) to achieve k-anonymity [13].…”

Section: Introductionmentioning

confidence: 99%

Users Collaborative Mix-Zone to Resist the Query Content and Time Interval Correlation Attacks

2018

Teh. vjesn.

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Abstract:In location-based services of continuous query, it is easier than snapshot to confirm whether a location belongs to a particular user, because sole location can be composed into a trajectory by profile correlation. In order to cut off the correlation and disturb the sub-trajectory, an un-detective region called mix-zone was proposed. However, at the time of this writing, the existing algorithms of this type mainly focus on the profiles of ID, passing time, transition probability, mobility patterns as well as road characteristics. In addition, there is still no standard way of coping with attacks of correlating each location by mining out query content and time interval from the subtrajectory. To cope with such types of attack, users have to generalize their query contents and time intervals similarity. Hence, this paper first provided an attack model to simulate the adversary correlating the real location with a higher probability of query content and time interval similarity. Then a user collaboration mix-zone (CoMix) that can generalize these two types of profiles is proposed, so as to achieve location privacy. In CoMix, each user shares the common profile set to lowering the probability of success opponents to get the actual position through the correlation of location. Thirdly, entropy is utilized to measure the level of privacy preservation. At last, this paper further verifies the effectiveness and efficiency of the proposed algorithm by experimental evaluations.

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Preventing velocity-based linkage attacks in location-aware applications

Cited by 95 publications

References 12 publications

AES-Route Server Model for Location based Services in Road Networks

AES-Route Server Model for Location based Services in Road Networks

The Trade-Off Between Privacy and Geographic Data Resolution. A Case of GPS Trajectories Combined With the Social Survey Results

Users Collaborative Mix-Zone to Resist the Query Content and Time Interval Correlation Attacks

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