A Data Mining Approach to Assess Privacy Risk in Human Mobility Data

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

doi:10.1145/3106774

Cited by 36 publications

(36 citation statements)

References 60 publications

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“…A different approach, which is based on privacy-by-design methodology, can be found in earlier studies [84,85,99]. Here, the framework PRUDEnce is presented, providing an approach that, before applying any privacy-preserving transformation, allows looking at the effective risk there is in the data, as well as the service or purpose for which the data are queried, instead of relying only on theoretical results in terms of privacy.…”

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

(So) Big Data and the transformation of the city

Andrienko

Boldrini

Caldarelli

et al. 2020

Int J Data Sci Anal

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The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.

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Section: Privacy-preserving Data Miningmentioning

confidence: 99%

(So) Big Data and the transformation of the city

Andrienko

Boldrini

Caldarelli

et al. 2020

Int J Data Sci Anal

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“…We present an exhaustive list of predetermined behavior dependent parameters used by popular ROI discovery techniques in Table I. These parameters are measures of individual mobility dynamics [27]. Therefore, in a situation where the adversary requests a data provider for aggregated/sparse mobility data, a knowledge of these parameters can increase the background knowledge to carry about membership inference attacks.…”

Section: The Parameter Cursementioning

confidence: 99%

Capstone: Mobility Modeling on Smartphones to Achieve Privacy by Design

Kulkarni

Moro

Chapuis

et al. 2018

2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/ 12th IEEE International

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Sharing location traces with context-aware service providers has privacy implications. Location-privacy preserving mechanisms, such as obfuscation, anonymization and cryptographic primitives, have been shown to have impractical utility/privacy tradeoff. Another solution for enhancing user privacy is to minimize data sharing by executing the tasks conventionally carried out at the service providers' end on the users' smartphones. Although the data volume shared with the untrusted entities is significantly reduced, executing computationally demanding server-side tasks on resourceconstrained smartphones is often impracticable. To this end, we propose a novel perspective on lowering the computational complexity by treating spatiotemporal trajectories as space-time signals. Lowering the data dimensionality facilitates offloading the computational tasks onto the digital-signal processors and the usage of the non-blocking signal-processing pipelines. While focusing on the task of user mobility modeling, we achieve the following results in comparison to the state of the art techniques: (i) mobility models with precision and recall greater than 80%, (ii) reduction in computational complexity by a factor of 2.5, and (iii) reduction in power consumption by a factor of 0.5. Furthermore, our technique does not rely on users' behavioral parameters that usually result in privacy-leakage and conclusive bias in the existing techniques. Using three real-world mobility datasets, we demonstrate that our technique addresses these weaknesses while formulating accurate user mobility models.Problem 1: Mobility Signal Generation. Given a trajectory T u of an individual u, a temporally ordered sequence of tuples, 3 Art. 23: www.privacy-regulation.eu/en/article-23-restrictions-GDPR.htm such that, T u = (l 1 ,t 1 ), (l 2 ,t 2 )...(l n ,t n ) , where l i = (lat i , lon i ), the latitude-longitude coordinate pair and t, the timestamp such that t i+1 > t i , translate T u into a 2-D signal S u (t), modeled as a function of changing distance with respect to time.Requirements and Challenges. (i) Constructing a continuous graph from the noisy and non-uniformly sampled location trajectories, (ii) preserving all the key knowledge contained in the trajectory samples, and (iii) retaining the spatial locality between the discretized points.Problem 2: Signal Interpretation. Given a user's spatiotemporal signal S u (t), interpret and model the distinct signal elements in the temporal and spectral domain, i.e. local maxima/minima, rising/falling edges, static signal component, candidate frequencies, spectral coefficients and harmonics, with respect to human mobility behaviors.Requirement and Challenge. In order to facilitate interdomain switching, attach and validate a semantic meaning to each of the above signal components.Problem 3: Mobility Modeling. Given the signal S u (t) and the valid interpretation of each element, construct the user's mobility model in terms of a graph G u (ROI, Tr), where ROI = {ROI 1 , ROI 2 ...ROI n } is the set of all the re...

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“…The framework used in [7] and [8] tries to mitigate the issue above performing the systematic assessment of empirical privacy risk concerning specific attacks on mobility data. In practice, the framework simulates an adversary that, for each individual, possesses the knowledge maximizing the privacy risk of that individual.…”

Section: Introductionmentioning

confidence: 99%

Modeling Adversarial Behavior Against Mobility Data Privacy

Pellungrini

Pappalardo

Simini

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.

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A Data Mining Approach to Assess Privacy Risk in Human Mobility Data

Cited by 36 publications

References 60 publications

(So) Big Data and the transformation of the city

(So) Big Data and the transformation of the city

Capstone: Mobility Modeling on Smartphones to Achieve Privacy by Design

Modeling Adversarial Behavior Against Mobility Data Privacy

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