How Others Compromise Your Location Privacy: The Case of Shared Public IPs at Hotspots

Vratonjic, Nevena; Huguenin, Kévin; Bindschaedler, Vincent; Hubaux, Jean-Pierre

doi:10.1007/978-3-642-39077-7_7

Cited by 13 publications

(11 citation statements)

References 24 publications

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“…For instance, sharing location of a user not only diminishes his own privacy but also the privacy of others [18]. Even sharing the locations sporadically can still make adversary identify the user [19].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Dey et al analyze the risk of age inference in OSNs, notably by relying on information posted by users' friends and friends-of-friends [7]. In the context of location privacy, Vratonjic et al show how mobile users connecting to location-based services from the same IP address can indirectly compromise the location privacy of others [27]. Olteanu et al study how users reporting co-locations with other users (e.g., on online social networks) can decrease others' location privacy [23].…”

Section: Related Workmentioning

confidence: 99%

On Non-cooperative Genomic Privacy

Humbert

Ayday

Hubaux

et al. 2015

Financial Cryptography and Data Security

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Abstract. Over the last few years, the vast progress in genome sequencing has highly increased the availability of genomic data. Today, individuals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on personal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacysensitive and highly correlated between relatives. Therefore, individuals' decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to protect and (ii) whether or not to reveal their genome. We study this tension by using a game-theoretic approach. First, we model the interplay between two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent influence diagrams that enable us to efficiently compute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing benefits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.

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“…Private information about, for example, user profile and her age can also be inferred from shared information on online social networks [6,17]. Mobile users, connecting to locationbased services from a same IP address, can also compromise the privacy of those who want to keep their location private [23]. The loss in privacy, due to other users, has also been shown in other contexts such as genomics [10,14].…”

Section: Related Workmentioning

confidence: 99%

Quantifying the Effect of Co-location Information on Location Privacy

Olteanu

Huguenin²,

Shokri³

et al. 2014

Privacy Enhancing Technologies

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Abstract. Mobile users increasingly report their co-locations with other users, in addition to revealing their locations to online services. For instance, they tag the names of the friends they are with, in the messages and in the pictures they post on social networking websites. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users' locations, thus further threatening their location privacy: as co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, with respect to an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose two polynomial-time approximate inference algorithms and we extensively evaluate their performance on a real dataset. Our experimental results show that, even in the case where the adversary considers co-locations with only a single friend of the targeted user, the location privacy of the user is decreased by up to 75% in a typical setting. Even in the case where a user does not disclose any location information, her privacy can decrease by up to 16% due to the information reported by other users.

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How Others Compromise Your Location Privacy: The Case of Shared Public IPs at Hotspots

Cited by 13 publications

References 24 publications

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

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

On Non-cooperative Genomic Privacy

Quantifying the Effect of Co-location Information on Location Privacy

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