TSBM: The Temporal-Spatial Bayesian Model for Location Prediction in Social Networks

Jia, Yunyi; Wang, Yuanzhuo; Jin, Xiaolong; Cheng, Xueqi

doi:10.1109/wi-iat.2014.98

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Recent revelations about NSA surveillance programs also show that this type of information is of great use for tracking and identifying individuals [30]. The dual problem, i.e., inferring location from social ties, has also been studied by the research community [31]- [33]. In [34], the authors exploit proximity information detected via Bluetooth, which is similar to co-location, to build an opportunistic ad-hoc localization algorithm by using intersection techniques similar to what we use in our attack.…”

Section: Related Workmentioning

confidence: 99%

Quantifying Interdependent Privacy Risks with Location Data

Olteanu

Huguenin

Shokri

et al. 2017

IEEE Trans. on Mobile Comput.

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Abstract-Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users' IP addresses also constitute a source of co-location information. 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, considering 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 some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.

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Section: Related Workmentioning

confidence: 99%

Quantifying Interdependent Privacy Risks with Location Data

Olteanu

Huguenin

Shokri

et al. 2017

IEEE Trans. on Mobile Comput.

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“…Researchers proposed hybrid approaches to incorporate both user-item rating dataset and other contextual information in different scenarios, including social network information and time information [14]. For instance, social trust or friend aware recommender approaches model trustworthiness or similarities of users.…”

Section: Related Workmentioning

confidence: 99%

Prediction of purchase behaviors across heterogeneous social networks

Wang

Liu

et al. 2015

J Supercomput

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Due to the development of web services, many social network sites, as well as online shopping sites have been booming in the past decade, where it is a common phenomenon that people are likely to use multiple services at the same time. On the one hand, previous research findings indicate the data sparsity issues of online shopping accounts, which is caused by the heavy-tailed distribution of user information. On the other hand, in social network sites, the personal information and the corresponding statuses of an account are abundant, and their genuineness is guaranteed either by the service provider, or by the willingness of the account owner to connect to his or her friends in reality. Making use of the correlation between accounts of a same individual is a crucial prerequisite for many interesting cross network applications, such as improving the recommendation performance of the online shopping sites using extra information from social network services. In this paper, we firstly propose a game-theoretic method to identify correlation accounts of individuals between social network sites and online shopping sites with stable matching model, incorporating account profiles as well as historical behaviors. Using the above account relationships, we then put forward a predicting method that combines heterogeneous social network information and online shopping information, to predict the purchasing behaviors. The results show that our method identifies up to 70 % of the correlation accounts between Facebook and eBay, one of the most popular social network sites and online shopping 123Prediction of purchase behaviors across heterogeneous... 3321 sites in the world, respectively. The experimental results also show that using the correlation account sets, the accuracy of our purchase predicting method outperforms the state-of-the-art methods by 5 %.

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“…The other category is based on popular movement histories [21], [22]. In this situation, mobility prediction focuses on group of similar mobility behavior instead of eliminating random movements from the entire body of mobile users' profiles.…”

Section: Introductionmentioning

confidence: 99%

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

2019

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Recently, mobility has gathered tremendous interest as the users' desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifthgeneration mobile system and puts forward some potential trends in the near future. INDEX TERMS Mobility prediction, quality of service (QoS), resource reservation, handover management, the fifth-generation mobile system (5G).

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TSBM: The Temporal-Spatial Bayesian Model for Location Prediction in Social Networks

Cited by 7 publications

References 20 publications

Quantifying Interdependent Privacy Risks with Location Data

Quantifying Interdependent Privacy Risks with Location Data

Prediction of purchase behaviors across heterogeneous social networks

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

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