Privacy-preserving inference of social relationships from location data

Shahabi, Cyrus; Fan, Liyue; Nocera, Luciano; Xiong, Li; Li, Ming

doi:10.1145/2820783.2820880

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…(3) Text Mining [5,22], Frequent Graph Pattern Mining [13] and Data Sequence Mining [1,2,15,23]. (4) Inference from Geo-location Data [12]. (5) Wrapping up the tutorial.…”

Section: Themementioning

confidence: 99%

Differential Privacy for Information Retrieval

Yang

Zhang

2017

Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval

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Information Retrieval (IR) research has extensively utilized personalization to advance its state-of-the-art. In this process, many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private information. However, while IR researchers are making progress, there is always a concern over violations to the users' privacy. Sometimes, the concern becomes so overwhelming that IR research has to stop to avoid leaking users' privacy. The good news is that there have been increasing attentions paid on the joint field of privacy and IR-privacy-preserving IR. As part of the effort, this tutorial offers an introduction to differential privacy (DP), one of the most advanced techniques in privacy research, and provides necessary set of theoretical knowledge for applying privacy techniques in IR. Differential privacy is a technique that provides strong privacy guarantees for data protection. Theoretically, it aims to maximize the data utility in statistical datasets while minimizing the risk of exposing individual data entries to any adversary. Differential privacy has been applied across a wide range of applications in database, data mining, and IR. This tutorial aims to lay a theoretical foundation of DP and how it can be applied to IR. We hope the attendees of this tutorial will have a good understanding of DP and the necessary knowledge to work on this newly minted joint research field of privacy and IR.

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“…(3) Text Mining [5,22], Frequent Graph Pattern Mining [13] and Data Sequence Mining [1,2,15,23]. (4) Inference from Geo-location Data [12]. (5) Wrapping up the tutorial.…”

Section: Themementioning

confidence: 99%

Differential Privacy for Information Retrieval

Yang

Zhang

2017

Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval

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“…Shahabi et al [19] proposed a framework which can attack social relationship from privacy preserving spatiotemporal datasets. However, existing attack models are mainly based on location entropy.…”

Section: Current Attack Modelsmentioning

confidence: 99%

Why You Go Reveals Who You Know: Disclosing Social Relationship by Cooccurrence

Feng

Wang

et al. 2017

Wireless Communications and Mobile Computing

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The popularity of location-based services (LBS) and the ubiquity of sensor device have resulted in rich spatiotemporal data. A large number of human behaviors had been recorded including cooccurrence which refers to the phenomenon that two people have been to the same places at the same time. These data enable attackers to infer people's social relationship based on their cooccurrences and many attack models were proposed. However, current attack models still cannot effectively address the following two challenges: How to distinguish cooccurrences between acquaintances and strangers? What kind of cooccurrence contributes to strong social strength? In this paper, we present a novel social relationship attack model-the Mobility Intention-based Relationship Inference (MIRI) model-which can solve the above two issues. Firstly, we extract mobility intentions and adopt them to characterize cooccurrences. A classification model is trained for attacking social relationship. The experimental results on two real-world datasets demonstrate that the proposed MIRI model can properly differentiate cooccurrences by simultaneously considering spatial and temporal features. The comparison results also indicate that MIRI model significantly outperforms state-of-the-art social relationship attack models.

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“…Given the sensitivity of detailed movement data of individuals in this research, the dataset contains no raw movement data of individuals. While the anonymity of movement data is hard to achieve [23], it contains patterns that can identify individuals. In fact, De Montjoye et al [18] studied fifteen months of human mobility data for one and a half million individuals and concluded that human mobility patterns are highly unique.…”

Section: Predictive Analytics For Improving the Accuracy Of Predictiomentioning

confidence: 99%

Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft

Amirian

Basiri

Morley

2016

Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science

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The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The navigation apps (often called "Maps"), use a variety of available data sources to calculate and predict the travel time as well as several options for routing in public transportation, car or pedestrian modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). In the paper, we will show that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual's movement profile. In addition, we will exemplify that those apps suffer from a specific data quality issue which relates to the absence of information about location and type of pedestrian crossings. Finally, we will illustrate learning from movement profile of individuals using various predictive analytics models to improve the accuracy of travel time estimation.

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Privacy-preserving inference of social relationships from location data

Cited by 17 publications

References 21 publications

Differential Privacy for Information Retrieval

Differential Privacy for Information Retrieval

Why You Go Reveals Who You Know: Disclosing Social Relationship by Cooccurrence

Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft

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