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2023
Conference article
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Explaining socio-demographic and behavioral patterns of vaccination against the swine flu (H1N1) pandemic
Punzi C., Maslennikova A., Gezici G., Pellungrini R., Giannotti F.
Pandemic vaccination campaigns must account for vaccine skepticism as an obstacle to overcome. Using machine learning to identify behavioral and psychological patterns in public survey datasets can provide valuable insights and inform vaccination campaigns based on empirical evidence. However, we argue that the adoption of local and global explanation methodologies can provide additional support to health practitioners by suggesting personalized communication strategies and revealing potential demographic, social, or structural barriers to vaccination requiring systemic changes. In this paper, we first implement a chain classification model for the adoption of the vaccine during the H1N1 influenza outbreak taking seasonal vaccination information into account, and then compare it with a binary classifier for vaccination to better understand the overall patterns in the data. Following that, we derive and compare global explanations using post-hoc methodologies and interpretable-by-design models. Our findings indicate that socio-demographic factors play a distinct role in the H1N1 vaccination as compared to the general vaccination. Nevertheless, medical recommendation and health insurance remain significant factors for both vaccinations. Then, we concentrated on the subpopulation of individuals who did not receive an H1N1 vaccination despite being at risk of developing severe symptoms. In an effort to assist practitioners in providing effective recommendations to patients, we present rules and counterfactuals for the selected instances based on local explanations. Finally, we raise concerns regarding gender and racial disparities in healthcare access by analysing the interaction effects of sensitive attributes on the model's output.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 621–635, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_31
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus
Metrics:
Punzi C., Maslennikova A., Gezici G., Pellungrini R., Giannotti F.
Pandemic vaccination campaigns must account for vaccine skepticism as an obstacle to overcome. Using machine learning to identify behavioral and psychological patterns in public survey datasets can provide valuable insights and inform vaccination campaigns based on empirical evidence. However, we argue that the adoption of local and global explanation methodologies can provide additional support to health practitioners by suggesting personalized communication strategies and revealing potential demographic, social, or structural barriers to vaccination requiring systemic changes. In this paper, we first implement a chain classification model for the adoption of the vaccine during the H1N1 influenza outbreak taking seasonal vaccination information into account, and then compare it with a binary classifier for vaccination to better understand the overall patterns in the data. Following that, we derive and compare global explanations using post-hoc methodologies and interpretable-by-design models. Our findings indicate that socio-demographic factors play a distinct role in the H1N1 vaccination as compared to the general vaccination. Nevertheless, medical recommendation and health insurance remain significant factors for both vaccinations. Then, we concentrated on the subpopulation of individuals who did not receive an H1N1 vaccination despite being at risk of developing severe symptoms. In an effort to assist practitioners in providing effective recommendations to patients, we present rules and counterfactuals for the selected instances based on local explanations. Finally, we raise concerns regarding gender and racial disparities in healthcare access by analysing the interaction effects of sensitive attributes on the model's output.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 621–635, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_31
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
doi.org 
| link.springer.com | CNR ExploRA
2020
Journal article
Open Access
Modeling Adversarial Behavior Against Mobility Data Privacy
Pellungrini R., Pappalardo L., Simini F., Monreale A.
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.Source: IEEE transactions on intelligent transportation systems (Online) (2020): 1–14. doi:10.1109/TITS.2020.3021911
DOI: 10.1109/tits.2020.3021911
Project(s): SoBigData-PlusPlus
Metrics:
Pellungrini R., Pappalardo L., Simini F., Monreale A.
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.Source: IEEE transactions on intelligent transportation systems (Online) (2020): 1–14. doi:10.1109/TITS.2020.3021911
DOI: 10.1109/tits.2020.3021911
Project(s): SoBigData-PlusPlus
Metrics:
See at:
IEEE Transactions on Intelligent Transportation Systems 
| ieeexplore.ieee.org | IEEE Transactions on Intelligent Transportation Systems | ISTI Repository | CNR ExploRA
2017
Journal article
Open Access
A data mining approach to assess privacy risk in human mobility data
Pellungrini R., Pappalardo L., Pratesi F., Monreale A.
Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.Source: ACM transactions on intelligent systems and technology (Print) 9 (2017): 31:1–31:27. doi:10.1145/3106774
DOI: 10.1145/3106774
Project(s): SoBigData
Metrics:
Pellungrini R., Pappalardo L., Pratesi F., Monreale A.
Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.Source: ACM transactions on intelligent systems and technology (Print) 9 (2017): 31:1–31:27. doi:10.1145/3106774
DOI: 10.1145/3106774
Project(s): SoBigData
Metrics:
See at:
ACM Transactions on Intelligent Systems and Technology | doi.acm.org | Archivio della Ricerca - Università di Pisa | ISTI Repository | ACM Transactions on Intelligent Systems and Technology | CNR ExploRA
2017
Contribution to book
Restricted
Assessing privacy risk in retail data
Pellungrini R., Pratesi F., Pappalardo L.
Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.Source: Personal Analytics and Privacy. An Individual and Collective Perspective, edited by Riccardo Guidotti, Anna Monreale, Dino Pedreschi, Serge Abiteboul, pp. 17–22, 2017
DOI: 10.1007/978-3-319-71970-2_3
Project(s): SoBigData
Metrics:
Pellungrini R., Pratesi F., Pappalardo L.
Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.Source: Personal Analytics and Privacy. An Individual and Collective Perspective, edited by Riccardo Guidotti, Anna Monreale, Dino Pedreschi, Serge Abiteboul, pp. 17–22, 2017
DOI: 10.1007/978-3-319-71970-2_3
Project(s): SoBigData
Metrics:
See at:
Lecture Notes in Computer Science | link.springer.com | CNR ExploRA
2017
Conference article
Restricted
Fast estimation of privacy risk in human mobility data
Pellungrini R., Pappalardo L., Pratesi F., Monreale A.
Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual's mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.Source: SAFECOMP 2017 - International Conference on Computer Safety, Reliability, and Security, pp. 415–426, Trento, Italy, 12 September 2017
DOI: 10.1007/978-3-319-66284-8_35
Project(s): SoBigData
Metrics:
Pellungrini R., Pappalardo L., Pratesi F., Monreale A.
Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual's mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.Source: SAFECOMP 2017 - International Conference on Computer Safety, Reliability, and Security, pp. 415–426, Trento, Italy, 12 September 2017
DOI: 10.1007/978-3-319-66284-8_35
Project(s): SoBigData
Metrics:
See at:
Lecture Notes in Computer Science | link.springer.com | CNR ExploRA