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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Ochoa, I, Calbusch, L, Viecelli, K, de Paz, J, Leithardt, V. & Zeferino, C. (2019). Privacy in the internet of things: A study to protect user's data in LPR systems using blockchain. In Ghorbani A., Ray I., Lashkari A.H., Zhang J., Lu R. (Ed.), 17th International Conference on Privacy, Security and Trust, PST 2019: Proceedings.: IEEE.
Exportar Referência (IEEE) 
O. I et al.,  "Privacy in the internet of things: A study to protect user's data in LPR systems using blockchain", in 17th Int. Conf. on Privacy, Security and Trust, PST 2019: Proc., Ghorbani A., Ray I., Lashkari A.H., Zhang J., Lu R., Ed., IEEE, 2019
Exportar BibTeX 
@inproceedings{i2019_1732206637409,
	author = "Ochoa, I and Calbusch, L and Viecelli, K and de Paz, J and Leithardt, V. and Zeferino, C.",
	title = "Privacy in the internet of things: A study to protect user's data in LPR systems using blockchain",
	booktitle = "17th International Conference on Privacy, Security and Trust, PST 2019: Proceedings",
	year = "2019",
	editor = "Ghorbani A., Ray I., Lashkari A.H., Zhang J., Lu R.",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/pst47121.2019.8949076",
	publisher = "IEEE",
	address = "",
	organization = "",
	url = "https://ieeexplore.ieee.org/xpl/conhome/8937293/proceeding"
}
Exportar RIS 
TY  - CPAPER
TI  - Privacy in the internet of things: A study to protect user's data in LPR systems using blockchain
T2  - 17th International Conference on Privacy, Security and Trust, PST 2019: Proceedings
AU  - Ochoa, I
AU  - Calbusch, L
AU  - Viecelli, K
AU  - de Paz, J
AU  - Leithardt, V.
AU  - Zeferino, C.
PY  - 2019
DO  - 10.1109/pst47121.2019.8949076
UR  - https://ieeexplore.ieee.org/xpl/conhome/8937293/proceeding
AB  - Over the past decade, smart crime-fighting solutions have been adopted by the major cities around the world. In this context, license plate recognition (LPR) systems have been used by public safety forces to monitor vehicle movement. However, current systems store vehicle location data indistinctly, without differentiating vehicles that are under criminal investigation from those that are not. This monitoring may be used to infer personal data about the owner of the vehicle, resulting in a violation of privacy by disregarding data protection laws. This paper presents a study about the use of technologies to ensure privacy in the Internet of Things and proposes a model to protect data collected by LPR systems. Our solution uses private blockchains regulated by smart contracts to ensure that the storage of data complies with current data protection laws.
ER  -