Faking contextual data for fun, profit, and privacy

Chow, Richard; Golle, Philippe

doi:10.1145/1655188.1655204

Cited by 94 publications

(66 citation statements)

References 10 publications

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“…The study provides insights into the deiciencies of existing solutions however, it did not analyze and compare the techniques quantitatively. Another study by Chow et al [8] proposed two features that could be used to diferentiate TMN dummy queries from real user queries.…”

Section: Related Workmentioning

confidence: 99%

Incognito

Masood

Vatsalan

Ikram

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Users leave a trail of their personal data, interests, and intents while suring or sharing information on the Web. Web data could therefore reveal some private/sensitive information about users based on inference analysis. The possible identiication of information corresponding to a single individual by an inference attack holds true even if the user identiiers are encoded or removed in the Web data. Several works have been done on improving privacy of Web data through obfuscation methods [7,12,18,32]. However, these methods are neither comprehensive, generic to be applicable to any Web data, nor efective against adversarial attacks. To this end, we propose a privacy-aware obfuscation method for Web data addressing these identiied drawbacks of existing methods. We use probabilistic methods to predict privacy risk of Web data that incorporates all key privacy aspects, which are uniqueness, uniformity, and linkability of Web data. The Web data with high predicted risk are then obfuscated by our method to minimize the privacy risk using semantically similar data. Our method is resistant against adversary who has knowledge about the datasets and model learned risk probabilities using diferential privacy-based noise addition. Experimental study conducted on two real Web datasets validates the signiicance and eicacy of our method. Our results indicate that the average privacy risk reaches to 100% with a minimum of 10 sensitive Web entries, while at most 0% privacy risk could be attained with our obfuscation method at the cost of average utility loss of 64.3%.

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Section: Related Workmentioning

confidence: 99%

Incognito

Masood

Vatsalan

Ikram

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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“…Many location obfuscation mechanisms have been proposed, including reducing the granularity of the location (generalization), adding noise to the geographical location, adding fake location information, hiding location information, and changing identifiers [1], [4], [11], [15], [17].…”

Section: B Location Obfuscationmentioning

confidence: 99%

“…After agreeing with the privacy and data use policies 4 , they were asked to log in to their Foursquare account and grant us access to their check-ins and friend lists. After this step, our application verified if the participants actually fulfilled the admission criteria and, if so, it allowed them to continue to the first (static) set of questions.…”

Section: B Online Surveymentioning

confidence: 99%

Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

Bilogrevic¹,

Huguenin²,

Mihaila³

et al. 2015

Proceedings 2015 Network and Distributed System Security Symposium

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Abstract-Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., "restaurant"). Such data might reveal some personal information about users beyond what they actually want to disclose, hence their privacy is threatened. In this paper, we study users' motivations behind location check-ins, and we quantify the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. By means of a targeted userstudy on Foursquare (N = 77), we show that the motivation behind Foursquare check-ins is a mediator of the loss of utility caused by generalization. Using these findings, we propose a machinelearning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for utility. Our results show that the model accurately predicts the loss of utility caused by semantic and geographical generalization; this model enables the design of utility-aware, privacy-enhancing mechanisms in location-based social networks.

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“…But generating effective dummy queries that divert the adversary is a difficult task [9], as they need to look like actual queries over space and time. An optimum algorithm for generating dummy queries is an open problem.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Location Privacy

Shokri

Papadimitratos

Theodorakopoulos

et al. 2011

2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems

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Abstract-Location-aware smart phones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to identify and track users. We address this problem by proposing the first, to the best of our knowledge, usercollaborative privacy preserving approach for LBSs. Our solution, MobiCrowd, is simple to implement, it does not require changing the LBS server architecture, and it does not assume third party privacy-protection servers; still, MobiCrowd significantly improves user location-privacy. The gain stems from the collaboration of MobiCrowd-ready mobile devices: they keep their context information in a buffer, until it expires, and they pass it to other users seeking such information. Essentially, the LBS does not need to be contacted unless all the collaborative peers in the vicinity lack the sought information. Hence, the user can remain hidden from the server, unless it absolutely needs to expose herself through a query. Our results show that MobiCrowd hides a high fraction of location-based queries, thus significantly enhancing user location-privacy. To study the effects of various parameters, such as the collaboration level and contact rate between mobile users, we develop an epidemic model. Our simulations with real mobility datasets corroborate our model-based findings. Finally, our implementation of MobiCrowd on Nokia platforms indicates that it is lightweight and the collaboration cost is negligible.

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Faking contextual data for fun, profit, and privacy

Cited by 94 publications

References 10 publications

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Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

Collaborative Location Privacy

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