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Collection and Analysis of Sensitive Data with Privacy Protection by a Distributed Randomized Response Protocol

Contributo in Atti di convegno
Data di Pubblicazione:
2024
Abstract:
The data collected from smart devices, the Internet of Things (IoT), and Smart Homes can be used for mining purposes and potentially benefit organizations with a large user base. The data collected from personal devices is intrinsically private and should be collected through a privacy-guaranteed mechanism. Local differential privacy solves privacy problems by collecting randomized responses from each user, and it does not need to rely on a trusted data aggregator/curator. It allows for building reliable prediction models on the collected amount of randomized data. The proposed approach utilizes the randomized response technique in a novel manner: it guarantees privacy to users during the data collection and simultaneously preserves the high utility of the analysis. It can be seen as a case of synthetic data generation by producing contingency tables (marginals) in a privacy-preserving mechanism. This article describes the proposed randomized response technique and discusses the motivating applications domains. It justifies why it satisfies the property of differential privacy and utility guarantees theoretically and through experimental analysis with excellent results.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
Randomized Response; Local Differential Privacy; Contingency Tables; Privacy protection; Distributed computation protocol
Elenco autori:
Faisal Imran; Rosa Meo
Autori di Ateneo:
MEO Rosa
Link alla scheda completa:
https://iris.unito.it/handle/2318/1947613
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1947613/1294153/ACM_SAC_2024__Track_on_Privacy_by_Design_in_Practice_CR.pdf
https://iris.unito.it/retrieve/handle/2318/1947613/1525665/3605098.3636024.pdf
Titolo del libro:
Proceedings of the 39th ACM/SIGAPP Symposium On Applied Computing
Progetto:
Future HPC & Big Data-finanziato con fondi PNRR MUR-M4C2-Investimento 1.4-Avviso"Centri Nazionali"-D.D.n.3138 del 16/12/2021 rettificato con DD n.3175 del 18/12/2021,codice MUR CN00000013, CUP D13C22001340001
  • Dati Generali
  • Aree Di Ricerca

Dati Generali

URL

https://www.sigapp.org/sac/sac2024/index.php; https://www.uni-saarland.de/lehrstuhl/sorge/forschung/workshopskonferenzen/acm-sac-2024-track-on-privacy-by-design-in-practice.html; https://dl.acm.org/

Aree Di Ricerca

Settori (13)


PE6_11 - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) - (2022)

PE6_5 - Security, privacy, cryptology, quantum cryptography - (2022)

CIBO, AGRICOLTURA e ALLEVAMENTI - Farmacologia Veterinaria

CULTURA, ARTE e CREATIVITA' - Culture moderne

INFORMATICA, AUTOMAZIONE e INTELLIGENZA ARTIFICIALE - Digitalizzazione della Cultura e della Creatività

INFORMATICA, AUTOMAZIONE e INTELLIGENZA ARTIFICIALE - Digitalizzazione della Società e della Pubblica Amministrazione

INFORMATICA, AUTOMAZIONE e INTELLIGENZA ARTIFICIALE - Industria X.0

INFORMATICA, AUTOMAZIONE e INTELLIGENZA ARTIFICIALE - Salute e Informatica

LINGUE e LETTERATURA - Linguistica

PIANETA TERRA, AMBIENTE, CLIMA, ENERGIA e SOSTENIBILITA' - Diritto dell'Ambiente

PIANETA TERRA, AMBIENTE, CLIMA, ENERGIA e SOSTENIBILITA' - Informatica e Ambiente

SCIENZE DELLA VITA e FARMACOLOGIA - Tecnologie Farmaceutiche e Cosmetiche

SCIENZE MATEMATICHE, CHIMICHE, FISICHE - Teorie e modelli Matematici
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