Preserving privacy in gps traces via uncertainty-aware path cloaking

Hoh, Baik; Gruteser, Marco; Xiong, Hui; Alrabady, Ansaf

doi:10.1145/1315245.1315266

Cited by 252 publications

(218 citation statements)

References 30 publications

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“…Consequently, several anonymity notions and methods for trajectories have been proposed [21,20,22,7,37,6,1,33,43,31,34,47,32,2,23,24]. Among those works, we next review the ones that are most similar to our approach, and we highlight our comparative advantages.…”

Section: Trajectory Anonymizationmentioning

confidence: 99%

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Microaggregation- and permutation-based anonymization of movement data

Domingo-Ferrer

Trujillo-Rasúa

2012

Information Sciences

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Movement data, that is, trajectories of mobile objects, are automatically collected in huge quantities by technologies such as GPS, GSM or RFID, among others. Publishing and exploiting such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows re-identification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span. Our distance measure can be naturally instantiated using other existing similarity measures for trajectories that are appropriate for anonymization purposes. Then, we propose two heuristics for trajectory anonymization which yield anonymized trajectories formed by fully accurate true original locations. The first heuristic is based on trajectory microaggregation using the above distance and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at location k-diversity. The strong point of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories. Experimental results on a synthetic data set and a real-life data set are presented; for similar privacy protection levels and most reasonable parameter choices, our two methods offer better utility than comparable previous proposals in the literature.

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Section: Trajectory Anonymizationmentioning

confidence: 99%

“…However, this work only considers sequential place visitation without real timestamps. If the adversary is assumed to use some prediction of continuation of a trajectory based on previous path and speed, then uncertainty-aware path cloaking [22,23] can suppress these trajectories; this however results in high information loss.…”

Section: Trajectory Anonymizationmentioning

confidence: 99%

Microaggregation- and permutation-based anonymization of movement data

Domingo-Ferrer

Trujillo-Rasúa

2012

Information Sciences

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“…Sampigethaya et al [28] use maximum tracking time to evaluate the location privacy of vehicles in VANET. Hoh et al [17] use the mean time to confusion to measure the privacy level of vehicles sending GPS traces in a traffic monitoring system. Fischer et al [8] propose to measure unlinkability of sender-message relations based on the outer and inner structures of the set partitions of the observed messages.…”

Section: Related Workmentioning

confidence: 99%

Measuring long-term location privacy in vehicular communication systems

Kargl

Weber

2010

Computer Communications

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a b s t r a c t Vehicular communication systems are an emerging form of communication that enables new ways of cooperation among vehicles, traffic operators, and service providers. However, many vehicular applications rely on continuous and detailed location information of the vehicles, which has the potential to infringe the users' location privacy. A multitude of privacy-protection mechanisms have been proposed in recent years. However, few efforts have been made to develop privacy metrics that can provide a quantitative way to assess the privacy risk, evaluate the effectiveness of a given privacy-enhanced design, and explore the full possibilities of protection methods.In this paper, we present a location privacy metric for measuring location privacy in vehicular communication systems. As computers do not forget and most drivers of motor vehicles follow certain daily driving patterns, if a user's location information is gathered and stored over a period of time, e.g., weeks or months, such cumulative information might be exploited by an adversary performing a location privacy attack to gain useful information on the user's whereabouts. Thus to precisely reflect the underlying privacy values, in our approach we take into account the accumulated information. Specifically, we develop methods and algorithms to process, propagate, and reflect the accumulated information in the privacy measurements. The feasibility and correctness of our approaches are evaluated by various case studies and extensive simulations. Our results show that accumulated information, if available to an adversary, can have a significant impact on location privacy of the users of vehicular communication systems. The methods and algorithms developed in this paper provide detailed insights into location privacy and thus contribute to the development of future-proof, privacy-preserving vehicular communication systems.

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“…Hoh [11] proposed an uncertainty-aware path cloaking algorithm to hide location samples in a dataset. As processing delay is a significant impediment in this method, Meyerowitz [12] tries to resolve it by predicting which intersection the users will pass through, and proactively retrieve data of the user's locations.…”

Section: Full Trust Centralized Third Partymentioning

confidence: 99%

“…Furthermore, the locations visited by the vehicles can be associated with the places of interests [7] by firstly accumulating the driving paths, then by cross-referencing the accumulation results with geographical maps or other location based services. Existing methodologies to enhance location privacy can be classified into several categories: k-anonymity [8][9][10], path confusion [11,12], and Mix-Zones [13,14]. These approaches are either of the type of centralized or partially centralized control and present two obvious drawbacks.…”

Section: Introductionmentioning

confidence: 99%

RSSI-Based User Centric Anonymization for Location Privacy in Vehicular Networks

Shan

2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. In Vehicular Networks, for enhancing driving safety as well as supporting other applications, vehicles periodically broadcast safety messages with their precise position information to neighbors. However, these broadcast messages make it easy to track specific vehicles and will likely lead to compromise of personal privacy. Unfortunately, current location privacy enhancement methodologies in VANET, including Pseudonymization, K-anonymity, Random silent period, Mix-zones and path confusion, all suffer some shortcomings. In this paper, we propose a RSSI (Received Signal Strength Indicator)-based user centric anonymization model, which can significantly enhance the location privacy and at the same time ensure traffic safety. Simulations are performed to show the advantages of the proposed method. In comparison with traditional random silent period method, our method can increase at least 47% of anonymity in both simple and correlation tracking.Keywords: VANET, Location Privacy, Tracking, Anonymity IntroductionNowadays, more and more vehicles are equipped with navigation systems that can provide drivers with the directions to the destination. To fulfill their safety functions, many safety-related applications require that the vehicles broadcast their current position, speed, and direction periodically. Because these safety messages are broadcasted wirelessly in plaintext for safety applications, they are vulnerable to eavesdropping, and the location information of the vehicles can then be extracted from either position related data or identification related data. Therefore, although the broadcast safety messages could in principle improve driving safety, unauthorized parties or attackers may exploit the vulnerabilities of these VANET application systems and compromise the location privacy of the interested vehicles [1].To solve the problem mentioned above, the authors in [2][3][4][5] proposed schemes to remove the correlation between locations and identifiers by periodically or randomly updating vehicles' pseudo identifiers. Although these methods could make vehicles unidentifiable within an anonymity set in motionless states, it can still be traced by movement tracking [6]. Furthermore, the locations visited by the vehicles can be associated with the places of interests [7] by firstly accumulating the driving paths, then by cross-referencing the accumulation results with geographical maps or other location based services. Existing methodologies to enhance location privacy can be

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Preserving privacy in gps traces via uncertainty-aware path cloaking

Cited by 252 publications

References 30 publications

Microaggregation- and permutation-based anonymization of movement data

Microaggregation- and permutation-based anonymization of movement data

Measuring long-term location privacy in vehicular communication systems

RSSI-Based User Centric Anonymization for Location Privacy in Vehicular Networks

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