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Protect your Data and I'll Show Its Utility: A Practical View about Mix-zones Impacts on Mobility Data for Smart City Applications

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Autor(es):
de Mattos, Ekler Paulino ; Domingues, Augusto C. S. A. ; Silva, Fabricio A. ; Ramos, Heitor S. ; Loureiro, Antonio A. F.
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: PROCEEDINGS OF THE INT'L ACM SYMPOSIUM ON PERFORMANCE EVALUATION OF WIRELESS AD HOC, SENSOR, & UBIQUITOUS NETWORKS, PE-WASUN 2023; v. N/A, p. 8-pg., 2023-01-01.
Resumo

When designing smart cities' building blocks, mobility data plays a fundamental role in applications and services. However, mobility data usually comes with unrestricted location of its corresponding entities (e.g., citizens and vehicles) and poses privacy concerns, among them recovering the identity of those entities with linking attacks. To address the privacy of users' identity, Location Privacy Protection Mechanisms (LPPMs) based on anonymization have been proposed, such as mix-zones. Once the data is protected, a comprehensive discussion about the trade-off between privacy and utility happens. However, issues still arise about the application of anonymized data to smart city development: what are the smart cities applications and services that can best leverage mobility data anonymized by mix-zones? To answer this question, we present a methodology that evaluates the utility in many aspects with metrics related to privacy, mobility, and anonymized trajectories produced by mix-zones. The results showed that the proposed methodology identifies application domains of smart cities in which anonymized data can have more or less utility. Additionally, different datasets present different behaviors in terms of utility. These insights can contribute significantly to the utility of both open and private data markets for smart cities. (AU)

Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
Beneficiário:Nelson Luis Saldanha da Fonseca
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 23/00721-1 - Quantificação de incerteza em aprendizado federado adversário
Beneficiário:Heitor Soares Ramos Filho
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 18/23064-8 - Mobilidade na computação urbana: caracterização, modelagem e aplicações (MOBILIS)
Beneficiário:Antonio Alfredo Ferreira Loureiro
Modalidade de apoio: Auxílio à Pesquisa - Temático