Analyzing Privacy Risk in Human Mobility Data

Pellungrini, Roberto; Pappalardo, Luca; Pratesi, Francesca; Monreale, Anna

doi:10.1007/978-3-030-04771-9_10

Cited by 7 publications

(5 citation statements)

References 26 publications

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“…The presence of predictive information, both socially and otherwise, has crucial implications. Privacy protections regarding social data are important to protect sensitive information about a user and their social ties [34,35]. Social information flow suggests that an individual's future movements can be predicted by studying the mobility patterns of a few acquaintances.…”

Section: Discussionmentioning

confidence: 99%

Contrasting social and non-social sources of predictability in human mobility

Chen,

Kelty,

Welles

et al. 2021

Preprint

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Social structures influence a variety of human behaviors including mobility patterns, but the extent to which one individual's movements can predict another's remains an open question. Further, latent information about an individual's mobility can be present in the mobility patterns of both social and non-social ties, a distinction that has not yet been addressed. Here we develop a "colocation" network to distinguish the mobility patterns of an ego's social ties from those of non-social colocators, individuals not socially connected to the ego but who nevertheless arrive at a location at the same time as the ego. We apply entropy and predictability measures to analyse and bound the predictive information of an individual's mobility pattern and the flow of that information from their top social ties and from their non-social colocators. While social ties generically provide more information than non-social colocators, we find that significant information is present in the aggregation of non-social colocators: 3-7 colocators can provide as much predictive information as the top social tie, and colocators can replace up to 85% of the predictive information about an ego, compared with social ties that can replace up to 94% of the ego's predictability. The presence of predictive information among non-social colocators raises privacy concerns: given the increasing availability of real-time mobility traces from smartphones, individuals sharing data may be providing actionable information not just about their own movements but the movements of others whose data are absent, both known and unknown individuals.

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Section: Discussionmentioning

confidence: 99%

Contrasting social and non-social sources of predictability in human mobility

Chen,

Kelty,

Welles

et al. 2021

Preprint

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“…Pellungrini et al [4] analyzed the privacy risk in human mobility data sets and proposed a series of attacks that could lead to serious privacy disclosure. As countermeasures, they removed the data entry from the data set with higher risk and then examine the data set with various collective measurements.…”

Section: Privacy Protection Methods On Trajectory Datamentioning

confidence: 99%

“…Pellungrini et al [4] concluded a privacy risk assessment model and proposed various attacks kind according to the background knowledge requirement. They categorized the background knowledge into three kinds: location visit fact, location visit frequency, and location visit probability.…”

Section: Privacy Risks On Trajectory Datamentioning

confidence: 99%

“…In this experiment, we simulate the privacy attack method proposed in [4] to examine whether our new method can protect the trajectories data from re-identification attack. We randomly selected 10 users from the data set and applied the Home and Work attack, which is considered the most common and fundamental step to re-identify the trajectories of the victim with background knowledge.…”

Section: Re-identification Attackmentioning

confidence: 99%

“…However, if the raw data collected from the vehicles or user's personal terminal device is published without proper treatment, it might lead to serious privacy Journal of Computer and Communications risk. A malicious attacker can query the public data set and extract specific victim user's personal information, including occupation, working location, lifestyle, and even interpersonal network [4]. Because of such privacy leakage, the victim might suffer from annoying advertising and fraud, or even encounter life safety threats in extreme cases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

Shao¹,

Feng²,

Li³

2021

JCC

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Trajectory data set is the indispensable foundation for constructing reliable Internet of Vehicles (IoV) service and location-based service (LBS), while it is likely to be abused by malicious attackers to infer user's privacy. In this paper, we propose a trajectory protection method based on stop points obfuscation, which can confront various privacy attacks and preserve the semantic information to achieve adequate utility. Two strategies for stop point selection are designed, including category-distance priority method and Markov matrix method. Our new method was analyzed and evaluated on a real-world trajectory data set. The experiment result shows that our method can improve the utility of the data set and provide multi-level privacy protection.

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