research-article

Anonymously forecasting the number and nature of firefighting operations

Authors:

Jean-François Couchot,

Christophe Guyeux,

Guillaume RoyerAuthors Info & Claims

IDEAS '19: Proceedings of the 23rd International Database Applications & Engineering Symposium

Article No.: 30, Pages 1 - 8

https://doi.org/10.1145/3331076.3331085

Published: 10 June 2019 Publication History

Get Access

Abstract

Predicting the number and the type of operations by civil protection services is essential, both to optimize on-call firefighters in size and competence, to pre-position material and human resources... To accomplish this task, it is required to possess skills in artificial intelligence, which are not usually found in a medium-sized fire department. However, such a request may be mandated, for example from specialized companies or research laboratories. This mandate requires the transmission of potentially sensitive information relating to interventions which is not intended to be publicly available. The purpose of this article is to show that a machine learning tool can be deployed and provide accurate results, using a learning process based on anonymized data. Learning on real but anonymized data will be performed using extreme gradient boosting, and the performance of each anonymization will be compared on the number and of interventions per day, and their type.

References

[1]

Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi (Eds.). ACM, 785--794.

Digital Library

Google Scholar

[2]

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith. 2006. Calibrating Noise to Sensitivity in Private Data Analysis. In Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006, Proceedings (Lecture Notes in Computer Science), Shai Halevi and Tal Rabin (Eds.), Vol. 3876. Springer, 265--284.

Digital Library

Google Scholar

[3]

Cynthia Dwork, Aaron Roth, et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9, 3-4 (2014), 211--407.

Digital Library

Google Scholar

[4]

Khaled El Emam and Luk Arbuckle. 2013. Anonymizing health data: case studies and methods to get you started. "O'Reilly Media, Inc.".

Digital Library

Google Scholar

[5]

Khaled El Emam, Fida Kamal Dankar, Romeo Issa, Elizabeth Jonker, Daniel Amyot, Elise Cogo, Jean-Pierre Corriveau, Mark Walker, Sadrul Chowdhury, Regis Vaillancourt, et al. 2009. A globally optimal k-anonymity method for the de-identification of health data. Journal of the American Medical Informatics Association 16, 5 (2009), 670--682.

Crossref

Google Scholar

[6]

Jaewoo Lee and Chris Clifton. 2011. How Much Is Enough? Choosing ∈ for Differential Privacy. In Information Security, 14th International Conference, ISC 2011, Xi'an, China, October 26-29, 2011. Proceedings (Lecture Notes in Computer Science), Xuejia Lai, Jianying Zhou, and Hui Li (Eds.), Vol. 7001. Springer, 325--340.

Digital Library

Google Scholar

[7]

Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam. 2007. L-diversity: Privacy beyond k-anonymity. TKDD 1, 1 (2007), 3.

Digital Library

Google Scholar

[8]

Pierangela Samarati and Latanya Sweeney. 1998. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical Report. technical report, SRI International.

Google Scholar

[9]

Jia Xu, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, Ge Yu, and Marianne Winslett. 2013. Differentially private histogram publication. The VLDB Journal---The International Journal on Very Large Data Bases 22, 6 (2013), 797--822.

Digital Library

Google Scholar

Cited By

View all

Mallouhy RGuyeux CJaoude CMakhoul A(2022)Predicting fire brigades' operations based on their type of interventions2022 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC55113.2022.9825380(606-610)Online publication date: 30-May-2022
https://doi.org/10.1109/IWCMC55113.2022.9825380
Mallouhy RGuyeux CJaoude CMakhoul A(2022)Machine Learning for Predicting Firefighters’ Interventions Per Type of Mission2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)10.1109/CoDIT55151.2022.9804035(1196-1200)Online publication date: 17-May-2022
https://doi.org/10.1109/CoDIT55151.2022.9804035
Mallouhy RGuyeux CJaoude CMakhoul A(2022)K-mean Clustering: a case study in Yvelines, Île-de-France2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN56167.2022.10008344(365-372)Online publication date: 4-Dec-2022
https://doi.org/10.1109/CICN56167.2022.10008344
Show More Cited By

Index Terms

Anonymously forecasting the number and nature of firefighting operations

Recommendations

k-anonymity: a model for protecting privacy

Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version ...
Achieving k-anonymity privacy protection using generalization and suppression

Often a data holder, such as a hospital or bank, needs to share person-specific records in such a way that the identities of the individuals who are the subjects of the data cannot be determined. One way to achieve this is to have the released records ...
Personalised anonymity for microdata release

Individual privacy protection in the released data sets has become an important issue in recent years. The release of microdata provides a significant information resource for researchers, whereas the release of person‐specific data poses a threat to ...

Comments

Information & Contributors

Information

Published In

IDEAS '19: Proceedings of the 23rd International Database Applications & Engineering Symposium

June 2019

364 pages

ISBN:9781450362498

DOI:10.1145/3331076

General Chairs:
Bipin C. Desai
Concordia University
,
Dimosthenis Anagnostopoulos
Harokopio University of Athens
,
Program Chairs:
Yannis Manolopoulos
Open University of Cyprus
,
Mara Nikolaidou
Harokopio University of Athens

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 June 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IDEAS 2019

IDEAS 2019: 23rd International Database Engineering & Applications Symposium

June 10 - 12, 2019

Athens, Greece

Acceptance Rates

Overall Acceptance Rate 74 of 210 submissions, 35%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
100
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mallouhy RGuyeux CJaoude CMakhoul A(2022)Predicting fire brigades' operations based on their type of interventions2022 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC55113.2022.9825380(606-610)Online publication date: 30-May-2022
https://doi.org/10.1109/IWCMC55113.2022.9825380
Mallouhy RGuyeux CJaoude CMakhoul A(2022)Machine Learning for Predicting Firefighters’ Interventions Per Type of Mission2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)10.1109/CoDIT55151.2022.9804035(1196-1200)Online publication date: 17-May-2022
https://doi.org/10.1109/CoDIT55151.2022.9804035
Mallouhy RGuyeux CJaoude CMakhoul A(2022)K-mean Clustering: a case study in Yvelines, Île-de-France2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN56167.2022.10008344(365-372)Online publication date: 4-Dec-2022
https://doi.org/10.1109/CICN56167.2022.10008344
Guyeux CMakhoul ABahi J(2022)How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ InterventionsIntelligent Systems and Applications10.1007/978-3-031-16072-1_41(558-572)Online publication date: 31-Aug-2022
https://doi.org/10.1007/978-3-031-16072-1_41
Elias Mallouhy RGuyeux CJaoude CMakhoul A(2022)Anomalies and Breakpoint Detection for a Dataset of Firefighters’ Operations During the COVID-19 Period in FranceInformation Systems and Technologies10.1007/978-3-031-04826-5_1(3-12)Online publication date: 11-May-2022
https://doi.org/10.1007/978-3-031-04826-5_1
Mallouhy RGuyeux CJaoude CMakhoul A(2022)Forecasting the Number of Firemen Interventions Using Exponential Smoothing Methods: A Case StudyAdvanced Information Networking and Applications10.1007/978-3-030-99584-3_50(579-589)Online publication date: 31-Mar-2022
https://doi.org/10.1007/978-3-030-99584-3_50
Arcolezi HCerna SGuyeux CCouchot J(2021)Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response TimeMathematical and Computational Applications10.3390/mca2603005626:3(56)Online publication date: 4-Aug-2021
https://doi.org/10.3390/mca26030056
Elias Mallouhy RGuyeux CAbou Jaoude CMakhoul A(2021)Time Series Forecasting for the Number of Firefighters InterventionsAdvanced Information Networking and Applications10.1007/978-3-030-75100-5_4(39-50)Online publication date: 24-Apr-2021
https://doi.org/10.1007/978-3-030-75100-5_4
Cerna SGuyeux CRoyer GChevallier CPlumerel G(2020)Predicting Fire Brigades Operational Breakdowns: A Real Case StudyMathematics10.3390/math80813838:8(1383)Online publication date: 18-Aug-2020
https://doi.org/10.3390/math8081383
Cerna SGuyeux CArcolezi HRoyer G(2020)Boosting Methods for Predicting Firemen Interventions2020 11th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS49469.2020.239488(001-006)Online publication date: Apr-2020
https://doi.org/10.1109/ICICS49469.2020.239488

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

k-anonymity: a model for protecting privacy

Achieving k-anonymity privacy protection using generalization and suppression

Personalised anonymity for microdata release