Forecasting the number of firefighter interventions per region with local-differential-privacy-based data

Arcolezi, Héber H.; Couchot, Jean-François; Cerna, Selene; Guyeux, Christophe; Royer, Guillaume; Bouna, Béchara Al; Xiao, Xiaokui

doi:10.1016/j.cose.2020.101888

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…We recall that for 2020, these statistics are up to June 2020 only. As also noticed in [7,8], the number of interventions increases throughout the years. The year 2010 presented high values in comparison with all other years, e.g., for Z1, the average ART was above the 10 min recommendation.…”

Section: Discussionmentioning

confidence: 55%

“…The medical literature has mainly focused attention on the analysis of ART [3,34,43] and its association with trauma [2,30] and cardiac arrest [1,4,6], for example. To reduce ART, some works propose reallocation of ambulances [5,44], operation demand forecasting [5,7,8,22,45], travel time prediction [12], simulation models [35,46], and EMS response time predictions [11,12]. The work in [11] propose a real-time system for predicting ARTs for the San Francisco fire department, which closely relates to this paper.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, in [18] the authors evaluate how some models can memorize sensitive information from the training data, and in [19], the authors investigate how ML models are susceptible to membership inference attacks. To address these problems, some works [7,[20][21][22][23][24][25]49] propose to train ML models with sanitized data, which is also known as input perturbation [26] in the privacy-preserving ML literature.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we analyzed EMS operations of the Departmental Fire and Rescue Service of Doubs (SDIS 25), which has 71 centers currently deployed across the Doubs region in France to attend to its population. As noticed in [7,8], the SDIS 25 and fire departments in general, have been facing a continuous increase in the number of interventions over the years, which may have adverse consequences on ARTs. For instance, the pre-travel delay directly affects ARTs if there is a lack of human and material resources when a call is received.…”

Section: Introductionmentioning

confidence: 99%

“…However, if the location is sanitized via GI, many other explanatory variables (e.g., distance, travel time, city) would be 'perturbed' too. In the privacy-preserving ML literature, training ML models with sanitized data is common practice [7,[20][21][22][23][24][25], which is also known as input perturbation [26]. In contrast to objective [27] and gradient [28] perturbation settings, input perturbation is the easiest method to apply, and it is independent of any ML and post-processing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

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Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

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Section: Discussionmentioning

confidence: 55%