Methods for Location Privacy: A comparative overview

Chatzikokolakis, Konstantinos; ElSalamouny, Ehab; Palamidessi, Catuscia; Pazii, Anna

doi:10.1561/3300000017

Cited by 25 publications

(26 citation statements)

References 62 publications

(100 reference statements)

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“…Because most of these data are personal and confidential (e.g., location), there is a need for privacy-preserving techniques for processing and using these data. Location privacy is an emergency research topic [13,14] due to the ubiquity of LBSs. Within our context, using and/or sharing the exact location of an emergency raises many privacy issues.…”

Section: Discussionmentioning

confidence: 99%

“…For example, by knowing that one intervention took place in front of the house of a debilitated person, attackers with auxiliary information may accurately infer that this person received care and (mis)use this information for their own good. Indeed, location privacy is an emerging and active research topic in the literature [13][14][15] as publicly exposing users' location raises major privacy issues. A common way to achieve location privacy is by applying a location obfuscation mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

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Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

View full text Add to dashboard Cite

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“…Location privacy. Location privacy has been widely studied in the literature, and its survey can be found in [35]. A number of location obfuscation methods have been proposed so far, and they can be broadly divided into the following four types: perturbation (adding noise) [36,3,5], location generalization (merging regions) [37,38], and location hiding (deleting) [37,39], and adding dummy locations [40,41,42].…”

Section: Related Workmentioning

confidence: 99%

Local Obfuscation Mechanisms for Hiding Probability Distributions

Kawamoto

Murakami²

2019

Lecture Notes in Computer Science

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We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy as the local differential privacy for probability distributions. Roughly, the distribution privacy of a local obfuscation mechanism means that the attacker cannot significantly gain any information on the distribution of the mechanism's input by observing its output. Then we show that existing local mechanisms can hide input distributions in terms of distribution privacy, while deteriorating the utility by adding too much noise. For example, we prove that the Laplace mechanism needs to add a large amount of noise proportionally to the infinite Wasserstein distance between the two distributions we want to make indistinguishable. To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output. Then we apply this mechanism to the protection of user attributes in location based services. By experiments, we demonstrate that the tupling mechanism outperforms popular local mechanisms in terms of attribute obfuscation and service quality.

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“…However, area cloaking is subject to triangulation attacks; if the user queries twice and obtains two different zones could reveal that the real location is close to the border between them …”

Section: Preliminary Knowledge and Related Workmentioning

confidence: 99%

Intelligent pseudo‐location recommendation for protecting personal location privacy

Huang

Deng

Xie

et al. 2019

Concurrency and Computation

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Summary Individuals' right to privacy includes control over access to their location information. With the advent of location‐based services and personal transport services (such as ridesharing), the risk of location privacy breaches is increased greatly. The potential negative effects of location privacy leakages include spam location‐based service flooding, threats to personal safety (such as physical attacks), and intrusion related to access to private places (such as homes and hospitals). Therefore, protecting the privacy of users' real locations is becoming increasingly important. This is often achieved using a pseudo‐location near the real location, but existing pseudo‐location generators, such as NRand and the uniform random method, suffer from statistical inference, which can infer the obfuscation domain to cover the real location. In this paper, we propose an intelligent pseudo‐location recommendation (IPLR) method to reduce the risk of a statistical inference attack. In IPLR, we generate a random substitute of the real location to attract the adversary and thus hide the real location. Then, the pseudo‐location is generated in the neighborhood of the random substitute location following a normal distribution; the random substitute location is changed frequently to confuse attackers. In particular, we define three levels of location privacy, ie, address level, street level, and district level, to evaluate the effectiveness of the IPLR method. Our experimental study using simulation data demonstrates that the proposed IPLR method achieves lower risk of location privacy leakage and higher probabilities of safety in all three levels of location privacy than NRand and the random method. It also demonstrates the effectiveness of the proposed IPLR to balance location privacy and service quality.

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Methods for Location Privacy: A comparative overview

Cited by 25 publications

References 62 publications

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Local Obfuscation Mechanisms for Hiding Probability Distributions

Intelligent pseudo‐location recommendation for protecting personal location privacy

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